foods Review Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis Graciela Artavia 1,*, Carolina Cortés-Herrera 1 and Fabio Granados-Chinchilla 2 ���������� ������� Citation: Artavia, G.; Cortés-Herrera, C.; Granados-Chinchilla, F. Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis. Foods 2021, 10, 1081. https://doi.org/10.3390/foods 10051081 Academic Editor: Angela Di Pinto Received: 26 February 2021 Accepted: 19 March 2021 Published: 13 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Centro Nacional de Ciencia y Tecnología de Alimentos, Sede Rodrigo Facio, Universidad de Costa Rica, San José 11501-2060, Costa Rica; carolina.cortesherrera@ucr.ac.cr 2 Independent Researcher, Suite 23C, C115A Street, Curridabat, San José 11801, Costa Rica; fabio.granados@ucr.ac.cr * Correspondence: graciela.artavia@ucr.ac.cr; Tel.: +506-2511-7215 Abstract: This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique’s principle and focused on said technologies’ applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample. Keywords: food and feed research; food chemistry; instrumental food analysis; nutritional quality; safety; authenticity; adulteration 1. Introduction Food is a primary animal and human need; thus, food quality and safety are critical areas of focus in food analysis [1]. As environment and behavior evolve, they will mod- ify food consumption practices [2–4]. While consumer trends nowadays gravitate toward healthier [5] and natural [6] food options, an increase in population and high food demand tend to increase the production of processed foods [7,8]. Government and official agencies, then, must concern themselves with enforcing guaranteed food labeling compliance, nutri- tional quality, and food origin (see, for example, [9–11]), as well as the public perception of health and dietary declarations (see, for instance, [12–16] to avoid unfair trade [17]. Food safety is another issue closely related to human and animal health. Forcefully, raw ingredients and foods must be monitored for some of these substances are contaminants of anthropologic origin [18], while others will result from food processing operations or storage [19]. On the other hand, the region of origin or purity of food products and crops (e.g., Pro- tected Designation of Origin and Protected Geographical Indication [20,21]) has demon- strated to be more relevant as consumers search for more specific products (e.g., olives [22], cocoa [23], cheese [24], and wines [25]). On a related note, food authenticity plays a significant role in food analysis, and some products, such as honey, have a relevant place in the economic pyramid [26]. It is then paramount to assess that the product sold is what the consumer is paying for (e.g., monofloral honey, made up of nectar belonging to a single plant, are more expensive than honey from diverse botanical sources [27]). Foods 2021, 10, 1081. https://doi.org/10.3390/foods10051081 https://www.mdpi.com/journal/foods https://www.mdpi.com/journal/foods https://www.mdpi.com https://orcid.org/0000-0003-4828-3727 https://www.mdpi.com/article/10.3390/foods10051081?type=check_update&version=1 https://doi.org/10.3390/foods10051081 https://doi.org/10.3390/foods10051081 https://creativecommons.org/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.3390/foods10051081 https://www.mdpi.com/journal/foods Foods 2021, 10, 1081 2 of 48 Finally, food adulteration can evoke unfair competition and deceit in the final con- sumer [28], decrease food quality, and even pose a health hazard [29,30]. The consumer and industry demands the research communities to implement assays that might help, in turn, device measures (including regulations) to address the issues mentioned above. This has prompted food scientists to search for new approaches and tools to respond to major current food quality, safety, and authenticity problems. As an analytical chemistry field investigating analytes using scientific instruments, instrumental analysis has continuously contributed to reaching these goals. As more sophisticated instrumental techniques become increasingly available in food and feed analysis laboratories, food analysis has advanced. It has permitted us to resolve unknowns more efficiently and accurately and explore new food research tools to assess relevant markers (e.g., through food metabolomics [31]). Herein we will explore analytical techniques as nuclear magnetic resonance (NMR), Polymerase Chain Reaction (PCR), Microwave Plasma Atomic Emission Spectrometer and Inductively Coupled Plasma coupled with Mass Spectrometry (MP-AES and ICP-MS), Iso- tope Ratio Mass Spectrometry (IRMS), Infrared (IR) and Raman Spectroscopy, OXITEST®, as an example of a food accelerated oxidation method, and, finally, Gas and Liquid Chro- matography (GC and LC) approaches (especially those using mass spectrometry detection). An exhaustive explanation of applications of the PCR technique in animal and human feeding was carried out to make known that it is a very valuable technique for this field. PCR applications in food analysis are lesser known techniques that have both quantitative and qualitative capabilities and can supplement other traditional methods. Hence, our intent is to broaden the perspective of its importance for the food industry and for those analysis laboratories that provide services and conduct research on such recent issues that generate controversy in one way or another as in the case of GMOs and allergens. On the other hand, chromatography techniques have numberless applications and has been highly documented. However, we wanted to expose those emerging analytes in food safety, mainly those that exists usually in low concentrations but can become a health risk such as acrylamide, amines biogenic, and allergens while portraying some of the experience in analyzing them. Hopefully, this review will serve as a primer for scientists to familiarize themselves and comprehend these techniques’ vast capabilities with selected real applications in the food and feed using the most representative and recent examples (Figure 1). The methods below represent approaches that have already been developed or are currently being implemented in our laboratories. Foods 2021, 10, 1081 3 of 48 Figure 1. Representation of selected analytical methods and targets to exemplify applications in quality, safety, and authenticity of foods. Each technique complements each other, from each a different amount and nature of information is gathered. Additionally, each technique can benefit from other instrument’s capabilities (e.g., Isotope Ratio Mass Spectrometry (IRMS) benefits from Liquid Chromatography (LC) or Gas Chromatography (GC) separation power). 2. Selected Instrumental Techniques and Their Applications 2.1. Nuclear Magnetic Resonance Spectroscopy (NMR) Nuclear magnetic resonance bases its principle on the momentary alignment of atomic nuclei (due to an externally applied magnetic pulse) within a sample. More potent instru- Foods 2021, 10, 1081 4 of 48 ments have been developed, including a new commercial model by Brüker of 1.2 GHz [32]. An excellent primer for NMR is the recent work by Hatzakis [33]. We also refer the reader to the two reviews regarding NMR in food authentication [34,35]. The constant improvements made in magnet potency has further applications in using this technique (Table 1). Table 1. Examples for NMR spectroscopy for food analysis. Matrix Application Method, Solvent Nucleus Frequency, MHz Reference Milk Mammalian milk Phospholipid fingerprinting 1H decoupling 31P 400 (161) [36] Skim milk Ionic strength effect on micellar casein during membrane separation and diafiltration 1D, D2O 31P 500 (202) [37] UHT Milk Authenticity 1D and 2D, HSQC, and HMBC, D2O 1H, 13C 400 (100) [38] Mammary gland secretory tissue and milk serum Two goat breeds 1D NOESY 1H 800 [39] Milk powder Fat content Time-domain transverse relaxation 1H 9 [40] Water Buffalo milk Conventional vs. biological feeding 1D, D2O, Carr–Purcell–Meiboom–Gill) pulse sequence 1H, 13C, 40P 400 (100, 161) [41] Milk Butyrate marker unknown fat blends 1D, broadband, and inverse gate decoupling 13C 400 (100) [42] Milk Holstein Friesian vs. autochthonous Italian cows 1D and 2D, DOSY, TOCSY, HSQC, HMBC 1H, 13C 400 (100) [43] Serum, urine, and liver Displaced abomasum in Holstein cows 1D NOESY, D2O 1H 600 [44] Formula milk Organic vs. conventional production 1D NOESY 1H 500 [45] Commercial bovine milk Chemical composition analysis 1D, T1 NOESY, D2O 1H 700 [46] Infant formula Phospholipid fingerprinting 1D, 1H decoupling 31P 400 (161) [47] Goat milk Mastitis and heat stress 1D, D2O 1H 600 [48] Beverages Alcoholic beverages Hazardous substances (diethyl phthalate or polyhexamethyleneguanidine, MeOH, ethyl carbamate) 1D, D2O, 1.5 mol·L−1 KH2PO4 pH 7.4 1H 400 [49] Grape juice Authenticity 1D, D2O, zgpr 1H 400 [50] Wine, beers, spirits Alcohol content 1D, one pulse 1H 45/400 [51] Coconut water Water-sugar mixtures 1D, zgesp, 1H 800 [52] Grape pulp Authenticity (adulteration with apple or cashew juice) 1D, Carr-Purcell-Meiboom-Gill pulse sequence 1H 15 [53] Processed coffee seeds Roasted and ground coffee Authenticity 1D, zg30 and ZGCPPR 1H 600 [54] Roasted and ground coffee Authenticity 1D, zg30, D2O 600 [55] Commercial coffee samples Quality and authenticity 1D, zg30, qNMR 1H 400 [56] Toxins/Biological samples Bovine blood Aflatoxin ingestion biomarker NOESY, D2O, and 250 mmol L−1 KH2PO4, pH 7.0 1H 700 [57] Dietary ingredients Food supplements Adulteration with phenolphthalein, sildenafil, fluoxetine, lorcaserin, orlistat, and sibutramine Qualitative, CD3CN:D2O; qNMR: MeOD 1H 500 [58] Food supplements Adulteration with phenolphthalein and sibutramine MeOD 1H 59.7 [59] Allergens Foods Adulteration with peanut, (2S,4R)-N-methyl-4-9 hydroxy-L-proline (marker) NOESY, CD3Cl, MeOD 1H 400/600 [60] 2.1.1. Advantages and Limitations of the Application As a universal detector, NMR has a definite advantage as multiple analytes/markers can be monitored simultaneously. A myriad of information can be drawn or extracted from a single sample measurement as several nuclei can be used to perform 2D/1D experiments and explore the association among them. NMR also has the advantage of being a relatively fast and versatile, non-destructive, and green approach (as only very small quantities of deuterated solvent are needed to prepare the sample before measurement, and most beverages can be analyzed directly with water suppression pulses). However, it will require a specialized technician that is adept in the NMR sample and instrument manipulation. Additionally, chemometric analysis during food analysis using NMR will be a requirement [61]. Though a less common application, NMR, under some conditions, will Foods 2021, 10, 1081 5 of 48 allow the user to even use a marker for quantitative analysis (see, for example, [56]). The most relevant disadvantage of NMR-based techniques is that depending on the marker(s) selected for analysis, sensitivity is somewhat limited. Hence, on occasion, researchers will need to complement NMR analysis with other techniques that allow trace analysis (e.g., liquid or gas chromatography). Low-field NMR spectroscopy examples herein (see Table 1) present an opportunity to replace costlier or destructive methods while using non- deuterated solvents [62]. Isotope-based applications using NMR have also been developed and applied to food analysis (see below). 2.1.2. Applications of NMR in Dairy/Milk Metabolomics and Chemical Fingerprinting To circumvent mass dependence during the assay, Nascimento and coworkers [40] extracted and analyzed the fat fraction of commercial dried milk products. The authors also corroborated their results obtained with NMR by constructing a multivariate model based on NIR. As butyrate occurs in triacylglycerols from milk, this molecule has been used as a marker to determine the composition of fatty acid blends containing milk and non-milk fat ingredients [42]. The authors indicated a high association among NMR data and obtained from a high-resolution GC FAME analysis. Li and coworkers [38] used metabolite analysis in bovine and caprine milk to deter- mine milk authenticity by discriminating milk to which soymilk has been added. As low as 2 mL/100 mL of soymilk adulteration could be discerned. Palma and coworkers [39] used mammary gland biopsies and milk serum from two different goat breeds (i.e., tol- erant/sensitive to feed restrictions). From the analysis of n = 50 metabolites, the authors found changes among the control and restricted-fed groups in both breeds, albeit with no differences between breeds. When formulating diets, milk producers should keep in mind these metabolic changes during the scarcity of pastures during the dry season, leading to seasonal weight loss. Recently, Salama and coworkers [48] observed changes in goat milk chemical profiles when animals were subjected to heat stress or treated with E. coli lipopolysaccharide (LPS). LPS febrile response was masked in heat-stressed goats. Citrate was increased by heat stress, whereas choline, phosphocholine, N-acetylcarbohydrates, lactate, and β-hydroxybutyrate could be considered putative inflammation markers. Tomassini and coworkers [43] demonstrated that choline (an essential nutrient in human development) and carnitine exhibited differences between two breeds (Holstein and autochthonous Italian breeds). The authors show that 1H-NMR can assess relevant metabolites involved in milk quality both from a nutritional and industrial perspective. Basoglu and coworkers [44] compared the metabolic status of healthy Holstein dairy cows with those suffering from displaced abomasum (a core component of the ruminant stom- ach that typically resides on the abdomen’s floor fills with gas and rises to the top of the abdomen). The author found the risk of fatty liver and ketosis incremented consid- erably in cows with displaced abomasum. Also, serum glycine and hippuric acid were found to be reduced in dairy cows with displaced abomasum. Corbu and coworkers [45] performed a metabolomics study on the compositional differences between organic and conventionally produced infant milk. Methionine content was significantly higher in organic-based formulas, which can be used as a convenient marker to differentiate each production system. Foroutan and coworkers [46] have thoroughly described the chemical composition of Canadian milk samples. Three distinct instrumental analysis techniques were used to evaluate different analytes (i.e., LC-high resolution mass spectrometry [HRMS] with direct flow injection for amino acids, biogenic amines, monosaccharides, acylcarnitines, diglyc- erides, triglycerides, phosphatidylcholines, lysophosphatidylcholines, and sphingomyelins, ceramides, and cholesteryl esters, LC-MS/MS for the profiling of fat and water-soluble vitamins and free fatty acids, and ICP-MS for trace elements). The authors were able to assign 95% of all visible peaks in the 1H-NMR to a specific, fully identifiable compound (for a total of n = 59 metabolites in ranges as low as µmol·L−1). Foods 2021, 10, 1081 6 of 48 Using 31P-NMR, García and coworkers [36] compared the phospholipid fingerprint of human, cow, camel, and mare and found species specificity. Camel and mare milk showed higher and lower phospholipid content, respectively. Sphingomyelin and plasmalogens were found in the µg·mL−1 range. Boiani and coworkers [37] explored two operation units’ effects during skim milk’s technological processing on the casein micellar structure. In particular, the author examined inorganic phosphate in the serum and the casein-associated phosphate in the retentate. The authors found that a combination of phosphate, calcium, and citrate succeeded in preserve both the casein phosphate nanocluster and micellar casein structure while maintaining retentate pH in the region of the original milk pH. 31P-NMR has been used as a routine tool to assess phospholipid content in infant formulas, identifying markers such as phosphatidylethanolamine and sphingomyelin found in the low mg/100 g levels [47]. Finally, water-buffalo milk metabolomics was used to assess differences between conventional and organic farming [41]. Neutral and phospholipid fractions were monitored for two years. A lower milk biosynthesis and lactose levels, phosphatidylcholine, mono-, and polyunsaturated fats characterized milk obtained from conventional feeding systems. Hence, the authors demonstrated that traditional to natural feeding regimes influenced the buffalo milk composition, with possible implications for dairy products’ sensory and nutritional properties. NMR has achieved considerable advances in metabolomics in dairy research. In this regard, we suggest Sundekilde and coworkers [63] and Maher and Rochfort [64] as primers. Besides, Yanibada and coworkers [65] have prepared a thorough paper regarding the sample preparation methods recommended before metabolomics analysis for bovine milk. Additionally, Bertram [66] highlighted the use of metabolomics in milk and meat to assess authenticity and quality. Recently, Markoska and coworkers [67] have reviewed the most relevant NMR contributions in studies regarding milk protein structural elucidation (espe- cially for β-lactoglobulin, α-lactalbumin, and casein and their fractions/subunits; proteins which have shown resistance to crystallization). Extremely relevant data for protein be- havior will determine macro milk behavior during processing. We also suggest the reader an interesting manuscript prepared by Foroutan and coworkers [46], which uses several analytical techniques (including NMR, LC-HRMS, LC-MS/MS, and ICP-MS) and evaluate their contributions to milk composition determination. 2.1.3. Selected Applications for NMR Recently, Schmitt and coworkers [60] recognized powdered peanuts even from mix- tures of similar food products (e.g., almond, hazelnut, walnut) using a marker at 3.05 ppm in the 1H NMR spectra. The authors identified the said marker as (2S,4R)-N-methyl-4-9 hydroxy-L-proline after confirmation by synthesis. This approach will serve yet another example of techniques used for allergen detection, a growing health issue. Hachem and coworkers [58] and Balayssac and coworkers [59] analyzed dietary supplements marketed for weight loss and sold over-the-counter. Despite being labeled as natural products, both groups of authors found contaminated products with non-declared pharmaceutically active ingredients. Coffee is a standard food product subjected to fraud and then requires authentication. Intuitively, applications for coffee authentication are focused on countries with a trajectory in coffee production. Herein, we included Kenya [56] and Brazil [54,55]. For example, both Ribeiro and coworkers [54] and Milani and coworkers [55] recognized common coffee adulterants in roasted and ground coffee such as barley, coffee husks, corn, rice, soybean, and wheat. On the other hand, Okaru and coworkers [56] focused on quality indicators for coffee and determined caffeine, 16-O-methylcafestol, kahweol, furfuryl alcohol, and 5-hydroxymethylfurfural. They also performed fingerprint analysis to evaluate differences among coffee grains from Coffea arabica L. and C. canephora Pierre ex A. Froehner. Electrolyte and sugar content makes coconut water a natural isotonic drink, an ef- fective hydrating beverage that is adequate even after a high physical performance [68]. Foods 2021, 10, 1081 7 of 48 Its pleasant smooth flavor has made coconut water a popular product. Due to its tropical providence and the need for processing due to export demand, coconut water is vulnerable to fraud. Richardson and coworkers [52] used the chemical shift of malic acid as a marker for quantifying the degree of adulteration (i.e., the addition of water and sugars) of Costa Rican coconut water. For an excellent primer regarding the quality, authenticity, and pota- bility criteria for coconut water, we suggest the reader toward a recent review by Burns and coworkers [69]. Monakhova and coworkers [49] used a non-targeted approach to determine if alcoholic beverages were produced illegally or informally and may contain hazardous substances. The authors could form a batch of n = 304 samples to determine that n = 7 of said samples exhibited divergent NMR profiles. These samples contained undesired contaminants such as diethyl phthalate or polyhexamethyleneguanidine, methanol, or ethyl carbamate. Isaac- Lam [51] used a low-filed NMR to assess ethanol content in wines, beer, and spirits using acetic acid and acetonitrile as internal standards. Grandizoli and coworkers [50] developed an NMR-based method for quality control assessment of commercial and homemade grape juices by directly analyzing the juice without any sample pre-treatment. The authors followed the acetate and ethanol signals as markers for grape juice quality during refrigeration and room temperature. Recently, Miaw and coworkers [53] explored using a low field NMR to assess grape pulp adulteration with less expensive juices such as cashew and apple juice. The authors were able to identify pure nectars and juices as well as mixtures thereof. The authors compared their results (i.e., analyzed the same samples) using other techniques and demonstrated that NMR constructed prediction models were superior to those performed using ATR-FTIR [70–72]. Ogunade and coworkers [57] demonstrated that AFB1 altered markers in plasma of lactating Holstein dairy cows (e.g., decreasing alanine, leucine, arginine, acetic acid, and increasing ethanol). Meanwhile, sequestering agents (i.e., sodium bentonite clay and Saccharomyces cerevisiae fermentation product) prevented said adverse effects and improved metabolic status. Though it is common to find routine evidence of mycotoxin in the feed [73,74] and the presence of mycotoxigenic fungi (see, for example, [75]), direct evidence of animal exposure to mycotoxins is needed in suspicious cases were veterinary findings were consistent with mycotoxicosis [76]. 2.2. Real-Time/Quantitative Polymerase Chain Reaction (PCR/qPCR) PCR is a molecular biological method that involves duplicating and analyzing specific DNA sequences. PCR allows determining whether a particular animal or plant DNA is present in a food product. As such, it can be useful in determining both accidental and intentional adulterations of foods by biological contaminants [77]. 2.2.1. Advantages and Limitations of the Application Though microscopy still the “gold standard” for the detection of Prohibited Animal Proteins or Products (2003/126/EC and 2002/178/EC) [78,79], PCR is a rapid, sensitive, and specific method. Though more expensive (e.g., primer design costs for each animal species must be considered), PCR-based techniques are not dependent on the analyst’s ex- pertise as histological analysis by microscopy [79]. Also, the amount of chlorinated solvent used during the separation of fragments and ingredients during stereoscopic/microscopy analysis is considerable (for some samples, as much as 250 mL per sample). Differentiation of terrestrial mammalian species by microscopy is dependent on the presence of animal hair. In this regard, modern microscopy techniques such as scanning electron microscopy (SEM) permits the identification of the structural markers of such structures with ease (including the creation of new benchtop, economic, SEM models; see, for example, Nikon NeoScope JCM-6000 Plus) [80–83]. On the other hand, PCR-based techniques require undamaged DNA to be successful. Some of the thermal treatments for feed can render DNA molecules unserviceable. As stated before, improvements in targets and qPCR techniques can already exist even after Foods 2021, 10, 1081 8 of 48 steaming or autoclaving [84]. Sensitivity-wise, both microscopy and PCR methods can reach similar levels [85]. Finally, Nesic and coworkers [86] compared to light microscopy and PCR methods and applied them to the ruminal fluid of cows fed feed containing MBM as possible forensic significance samples. However, particles of animal origin in the ruminal fluid were detected only by microscopy. In the case of Salmonella analysis, for laboratories that already have an established enrichment-based method, PCR based techniques can be used as a screening technique or a second technique to improve the chances of identifying Salmonella-positive feed samples. The PCR-based molecular serotyping method can be done directly with the enriched culture medium and provides a simple and rapid detection and identification for Salmonella isolates. Pure culture serotyping for Salmonella may typically take 5–10 days for the entire process. Another advantage is that PCR is not dependent on utilizing a substrate or antigens’ expression, thereby circumventing the phenotypic variations in biochemical patterns and lack of detectable antigens [87]. PCR inhibitors and DNA loss during sample processing have already been described during particular feed sample analysis [88,89]. Similar techniques have been developed for the simultaneous detection of multiple pathogens in food for human consumption (see, for example, [90,91]). In yet another area of application, official methods such as those stated in the GMOMETH- ODS database [92] are based on the qualitative analysis of modified DNA sequences using PCR and loop-mediated isothermal amplification (LAMP). These methods are the basis for the enforcement of GMO-related legislation for food/feed control as they have been tailored to detect the unauthorized use of GMOs. As research in allergens grows more robust, more accuracy and sensibility are required from the available analytic and molecular techniques. This is especially relevant to some allergens, as peanut, tree nuts, and milk being the foremost allergens in most countries [93]. However, considerable variation occurs in the individual threshold dose among those with a given food allergy type. For example, from controlled clinical challenge trials in a study of 450 peanut-allergic individuals, peanut thresholds (NOAELs) for peanut-allergic individuals ranged from 0.1 mg up to perhaps 8 g of the whole peanut (an individual peanut can weigh from 500–800 mg) [94]. Westerhout and coworkers [95] recently have derived individual threshold doses from objective symptoms from clinical food challenge data (i.e., based on diagnostic medicine). The work is instrumental as an example of colossal international effort and insights into thresholds (NOAEL-LOAEL). Data obtained through specialized techniques are vital to threshold establishment and will improve the value for precautionary allergen labeling and produce quantitative data to aid in risk assessment [96–98]. Several international cohorts and task forces have been assembled in the effort to establish these limits, for example, ILSI Food Allergy—ILSI Europe/iFAAM, EFSA (whose scientific panel has provided several opinions (e.g., [99])), Australia and New Zealand Allergen Bureau’s VITAL®, UK Food Standards Agency, FoodDrink Europe, Netherlands Organisation for Applied Scientific Research and the Food Allergy Research and Resource Program at the University of Nebraska-Lincoln to name a few. DNA-based methods have unparalleled specificity and allow unequivocal identifica- tion of most allergens. Additionally, as demonstrated above, multi-allergen screening is not only possible, but it is a trend. In contrast, by its antibody-based nature alone, it cannot be specific. Additionally, enzyme-linked immunosorbent assay (ELISA) has limitations discriminating several allergen targets from other food components (e.g., celery, fish, and almonds). However, PCR is not the preferred method for other allergens: Egg cannot be identified with acceptable sensitivity and specificity, and gluten, a protein, cannot be detected [100]; though methods do exist and coding genes for gliadin (e.g., GliB1.1 and GliB1.2) can be used as targets [101,102]. However, ELISA is a widespread method for detecting and quantitating allergens due to its speed and ease of use, whereas PCR requires a dedicated laboratory space and trained personnel. Foods 2021, 10, 1081 9 of 48 Due to the increased number of recognized beer-spoilage species [103–105], each species’ evaluation will be excessively laborious and time-consuming. However, mul- tiplex PCR-based techniques permit the simultaneous identification of several known beer-spoilage species. One major drawback of using DNA-based techniques to assess bacteria is the inability to discriminate between viable and non-viable cells. Hence, molec- ular viability analyses have been developed to circumvent such issues [106]. Notably, in addition to the microorganisms stated above, Enterobacteria (see above) analysis could be used in the beverage industry as the survival of enteric pathogens in beer has been demonstrated [107]. 2.2.2. Detection of Animal Products in Feeds (Prohibited Animal Proteins or Products/PAPs) Most countries inspect their feeds for PAPs as they abide by Commission Regulation (EC) 999/2001, which prohibits the inclusion of protein derived from mammals in feeds for ruminants as PAP produced from ruminant carcasses, is assumed to be the transmis- sion route of Bovine Spongiform Encephalopathy. In this context, molecular assays allow for the automated semi-quantification of nucleic acids, especially when coupled with fluorescent probes. Fumière and coworkers [108] tested cattle materials treated at 134 ◦C in various feed matrices down to a limit of detection of about 0.1 g/100 g. The technique was successfully applied to well-characterized meat and bone meal (MBM) samples heated to as high as 141 ◦C. Similarly, Prado and coworkers [109] exemplified this technique’s application during one of the early interlaboratory studies. Cawthraw and coworkers [110] detected mammalian and avian mitochondrial DNA 16S rRNA genes in animal feed samples. Identification was performed using the 16S rRNA gene from bovine, ovine, porcine, and avian species. Additionally, vegetable-based feed standards spiked with MBM generated with a commercial rendering process, detecting as low as 0.1 g/100 g MBM. The PCR-based assay was compared with a microscopic examination for a wide range of commercial in several animal feed samples (n = 14,678). In this case, n = 111 (0.76%) samples examined contained prohibited material. However, most did not represent noncompliance as they were cataloged as pet food. Golinelli and coworkers [84] used qPCR to demonstrate its potential for use in the rapid and routine detection of the presence of bovine, caprine, ovine, porcine, and avian (chicken) MBM in feedstuffs. Kim and Kim [111] developed a multiplex PCR assay using species-specific primer sets and a universal eukaryotic primer for the simultaneous identification set in processed jerky products without DNA extraction. The resulting method showed no cross-reactivity against 16 animal species. It detected as low as 0.1 g/100 g pork in adulterated beef jerky and 0.1 g/100 g chicken in adulterated duck jerky. The meat species of commercial food and feed jerky products and could identify the meat species in commercial jerky products were successful. Finally, in a recent research, Marchetti, and coworkers a high percentage of seafood was mislabeled within the Italian markets [112]. A multi-marker DNA barcoding approach provided reliable and accurate discrimination of shark samples. Using two mitochondrial regions, COI and NADH2, the authors were able to uncover food fraud. This is an essential effort in species conservation and regulating illegal, unreported, and unregulated fishing [112]. Commercial test systems have been already sold as a companion for RT-PCR appli- cations (e.g., r-biopharm SureFood® ANIMAL ID/ANIMAL QUANT tests can be used to prove whether a particular food contains DNA from cattle, chicken, goat, pig, turkey, water buffalo, horse, rabbit, cat, donkey, sheep, and dog). In comparison, there are some commercial tests based on ELISA (e.g., r-biopharm ELISA-TEK™ and MELISA-TEK™). Similar techniques have also been used to assess meat’s adulteration for human consumption (see, for example, [113,114]). Besides, to this day, some reference materials re- garding species identification have been successfully produced to test laboratory adeptness (for example, the Dem Deutschen Referenzbüro für Ringversuche & Referenzmaterialien, Foods 2021, 10, 1081 10 of 48 which organize proficiency testing schemes for the determination in animal species for food matrices such as gelatin and candy). 2.2.3. Salmonella spp. in Feeds Because some significant outbreaks of human salmonellosis have been traced to contaminated animal feed, rapid and efficient Salmonella detection in feed is essential. The determination of Salmonella in feeds by immunological or culture-based methods is quite widespread [115]. However, RT-PCR-based techniques to assess Salmonella in animal feed became widespread [88,116,117]. In 2014, the European Union organized an interlaboratory comparison study for Salmonella in animal feed [118]. Finally, an excellent primer comparing both approaches in Salmonella from products of porcine origins can be found in the work by Castagna and coworkers [119]. More recently, Bonilauri and coworkers [120] performed a comparison to detect Salmonella spp., Listeria monocytogenes, and thermophilic Campylobacter spp. in several foods; in all cases, the prevalence was found to be higher using PCR-based techniques. Soria and coworkers [89] compared two PCR-based methods with several enrichment- based ones. Performance-wise, no method showed significant differences among each other for the identification and isolation of S. pullorum and S. gallinarum in poultry feed. However, the Salmocyst® broth method showed a 10-fold increase in sensitivity (i.e., as low as 30 CFU/25 g). D’Agostino and coworkers [121] developed a LAMP-based method for the analysis of animal feed for the presence of Salmonella spp. LAMP-positive signals are considered presumptive of the presence of Salmonella. Simultaneously, confirmation is obtained by continuing ISO 6579, whereas negative reactions can be regarded as signifying that the sample does not contain viable Salmonella. With this approach, uncontaminated samples can be identified 24 h less than using the ISO standard method alone; n = 13 from the n = 79 animal feed samples tested were Salmonella-positive. Benahmed and coworkers [122] evaluated the U.S. Food and Drug Administration Bacteriological Analyt- ical Manual lactose broth pre-enrichment medium and peptone buffered water coupled with real-time PCR assays to detect Salmonella enterica subsp. enterica serovar Cubana in naturally contaminated chick feed. After 24 h of incubation, the qPCR method was as sensitive as the culture method when modified buffered peptone water was used as the pre-enrichment medium but less sensitive than culture when lactose broth was used. After 48 h of incubation, the detection of Salmonella Cubana by qPCR and culture in ei- ther pre-enrichment medium was equivalent. This indicates that incubation time during pre-enrichment is essential to obtain better results for qPCR. The authors also compared the traditional serotyping method (which uses Salmonella’s pure cultures grown on blood agar). Beaubrun and coworkers [123] used the above approach to assess sensitivity in matrixes such as wheat bran, horse feed, dried molasses, calf milk replacer, dried beet pulp, and whole oats. The multiplex PCR serotyping assay applied proved to have ap- plication as a rapid screening and serotyping of Salmonella contaminating animal feed spiked with as low as 10 CFU/25 g and 2.5 CFU/25 g of Salmonella enterica serovars Ty- phimurium, Agona, and Hadar. Salazar and coworkers [124] analyzed n = 21 samples collected from layer hen farms and backyard layers from Ecuador. The researchers used ISO 6579 as a guideline for isolation and PCR to determine strain serotypification. With a high prevalence of contamination (percentages ranging from 33 to 76% from environmental surfaces cloacal swabs, feed, and feces samples), the dominant serovars detected were S. Infantis and S. Typhimurium. Heymans and coworkers [125] used a multiplex qPCR to assess S. Typhimurium and S. Enteritidis in several foodstuffs, including animal feed. Pre-enrichment was based on reference method ISO 6579 and a modified version using a Semisolid Rappaport-Vassiliadis medium. Exclusivity for strains tested ranged from 94.6 and 100%, whereas a limit of detection of 40 CFU/25 g was obtained (i.e., similar limit as for the culture-based method). Magossi and coworkers [126] tested 11 feed mills from 8 US states, and 12 sites were sampled within each facility. Samples were investigated both by culture and by PCR analysis for Salmonella enterica. From n = 237 samples, 66% Foods 2021, 10, 1081 11 of 48 resulted in culture-positive, and 13.1% were PCR positive, demonstrating the potential role as a vehicle for pathogen transmission and spread into the food production chain. Samar and coworkers [127] also compared traditional techniques and PCR to assess Salmonella in vegetables and forages. Again, commercial test kits have been developed to aid in the fast and semi-automated Salmonella assays by RT-PCR (e.g., r-biopharm SureFast® Salmonella species/Enteritidis/ Typhimurium 4 plex, Bio-rad® iQ-Check Salmonella II PCR, Noack Salmonella spp. Real- Time PCR, Kylt® Salmonella spp.). Recently, PCR techniques have been used to assess multiple foodborne bacterial pathogens [128,129], and other DNA-based methods (nucleic acid sequence-based amplification, LAMP, or DNA microarray) have been reported [130]. Application of PCR in food safety considering other relevant pathogenic Enterobacteriaceae has also been described (see, for example, [131]). 2.2.4. Detection of Genetically Modified Organisms/GMOs In the work of Fraiture and coworkers [132], the reader can find an excellent primer for GMOs’ detection approaches. On the other hand, Petrillo and coworkers [133] presented DNA target GMO-related sequences by screening public nucleotide sequence databanks by in silico determination of PCR amplification with reference methods for GMO anal- ysis, pooling, and collecting publicly available sequences related to GMOs in food and feed. The JRC GMO-Amplicons database is composed of more than 240,000 amplicons. Barbau-Piednoir [134] used data generated by sequencing and exploited it using BLAST to develop a pragmatic qPCR method to analyze feed additives for the presence of unau- thorized a B. subtilis (a vitamin B12 overproducing GMO). Mano and coworkers [135] validated a GMO detection method based on RT-PCR and increased 10-fold the sensitivity commonly achieved. Commonly introduced target sequences such as 35S [136] and NOS (i.e., Agrobacterium tumefaciens nopaline synthase terminator) were selected. Amplification plots obtained demonstrated that the method could discriminate among several GMO- maize events and non-GMO samples (i.e., maize, soybean, canola, and rice), could be applied to processed foods, and with no inhibition. Safei and coworkers [137] recently developed a PCR method for the detection of GM rice. The authors applied the method to n = 81 non-labeled rice samples from Iran. The cauliflower mosaic virus (CaMV) 35S pro- moter and NOS were selected as screening targets for the GM rice sequences. Of the samples tested, n = 2 out of 81 (2.4%) samples tested were positive for the CaMV 35S promoter. Most commercial kits designed to be applied in the laboratory with PCR are based on CaMV-P35S. Some examples include Promega Maxwell® PureFood GMO, Kogenebiotech PowercheckTM (relying on fluorophores like FAM, VIC/HEX, ROX/NED, and Cy5 to improve sensitivity), and PrimerdesignTM GenesigTM CaMV 35S Promoter GMO Event quantification kit. Additionally, method ISO 21571 deliver researchers flexibility and can be suitable for an extensive range of matrices [92]. 2.2.5. Allergens Food allergies are an essential health concern worldwide. The presence of undeclared allergenic ingredients or traces of allergens due to accidental contamination during food processing poses a significant health risk to sensitized individuals. Allergic reactions in children can swiftly progress to anaphylaxis, involving respiratory, cardiovascular, or gastrointestinal symptoms [138]. From the regulatory standpoint, Directive 2000/13/EC (which requires manufacturers to declare all ingredients present in pre-packaged foods sold in the EU) has been amended twice concerning allergens (i.e., 2003/89/EC and 2007/68/EC). Other legislative provi- sions related to food safety include Regulation 1169/2011 and 178/2002. Finally, UNE-EN 15842:2020 and CEN/TR 16338:2012 provide information on the criteria that must be met by molecular biology methods in detecting food allergens. The summary of the selected application of molecular diagnostics for food allergens using PCR is summarized in Table 2. Foods 2021, 10, 1081 12 of 48 Table 2. Selected method details for applications of PCR in allergens. Organism/Target Gene Primer Sequence (5′-3′) Amplicon Size, bp Reference Single allergen analysis Bos taurus α-lactalbumin α-LA-F: CACCCAGGCTGAACAGTTAACA α-LA-R: TCCGTAGCCCTTCAAGTCTTTC Probe: FAM-AGGTGTTCCGGGAGC-MGB 67 [139] Bos domesticus 12S rRNA gene 916-F: GTACTACTAGCAACAGCTTA 916-R: AGACTGTATTAGCAAGAATTGGTG Probe: FAM-TCTAGAAGGATATAAAGCACCGCCAAGT-BHQ1 EUK-F: AGCCTGCGGCTTAATTTGAC EUK-R: CAACTAAGAACGGCCATGCA Probe: FAM-AGGATTGACAGATTGAG-BHQ2 18SRG-F: CTGCCCTATCAACTTTCGATGGTA 18SRG-R: TTGGATGTGGTAGCCGTTTCTCA 121, 120, 113 [140] Multiplex allergen analysis soy28k (soybean/Glycine max (L.) Merr.), 2S albumin (sesame/Sesamum indicum L.), and Ara h 1 (peanut/Arachis hypogaea L.) Soy28k-F: CTAGAAACATTGGAAACACC Soy28k-R: ATCACATACCCTCAAGACAT Ses i 1-F: TGAGGAACGTGGACGAGAG Ses i 1-R: CCCTAGCCCTCTGGTAAACC Ara h 1-F: CCATCATTTCACCATCCACAC Ara h 1-R: CTCTCATTGCTCCTGCTACTA 18S rRNA-F: TCTGCCCTATCAACTTTCGATGGTA 18S rRNA-R: AATTTGCGCGCCTGCTGCCTTCCTT 147, 126, and 82 [141] Peanut (Ara h 1) and hazelnut/Corylus avellana L. (Cor a 1) Ara h 1-F: AGAGGGAGATATCACCAACCCAATC Ara h 1-R: GAGTTGAAGTGTGGGAGCATCAAAG Cor a 1-F: AAAGGCCATCAAGAGCATTG Cor a 1-R: CATCGCCTTCAATCACACTG Chloroplast-F: CGGACGAGAATAAAGATAGAGT Chloroplast-R: TTTTGGGGATAGAGGGACTTG 180, 258, 124 [142] Tomato (Cyclophilin), Apple (Mdtl 1), Peach (Pru p 2.01A), and Kiwi (Pectin methylesterase inhibitor). Pru-F: GCAACCGGAATTAGCAAC Pru-R: AAATCTTGACCCCCGTTCTC 18S rRNA-F: CGAAAGCATTTGCCAAGGAT 18S rRNA-R: CCGGAACCCAAAGACTTTGA Sola 5-F: GGAGCCAAATTCAACGATG Sola 5-R: ACGACGTGCTTTCCGTTGA Act 6-F: AAATCTGTCCCAAAACTCGC Act 6-R: TTAGCACTGGCCTGAGCTAT Mal 2-F: CTTGCCTTGCGTTTGGTGAT Mal 2-R: GGCACTGCTTCTCAAAGATCTCA 209, 172, 146, 127, and 105 [143] Wheat, buckwheat, and peanut F: CAT GGT GGG CGT CCTC R: AAA GGC CAT AAT GCC AGC TG Probe: FAM-CGG ATG CAC TGC ITT GAT AAA G-MGB F: CGT TGC CGA GAG TCG TTC TGT TT R: CGC CAA GGA CCA CGA ACA GAA G Probe: FAM-CGG GAC GCG CTT C-MGB F: TTG GTT CAA AGA GAC GGG CTC R: CAC GAG GGT TGT TCT CGA CC Probe: FAM-ACC GCG GCA GAT GG-MGB 64, 101, and 71 [144] Therefore, reliable analytical methods are required to detect and identify allergenic ingredients in food products. Real-time PCR allows specific and accurate amplification of allergen sequences. We refer the reader to Linacero and coworkers [145]. The authors review the most recent PCR-based techniques used to determine tree nuts and evaluate the effect of processing and unit operation. Sharma and coworkers [146] have already summarized the most common allergens and techniques (including immunoassay, DNA, and chromatographic-based approaches to assess). Renčová and coworkers [142] developed a multiplex PCR method to analyze peanut and hazelnut allergens in food matrices. The authors were able to remove inhibition using a 124 bp amplicon (the universal plant primers of chloroplast gene). The resulting method was able to detect as low as 10 mg·kg−1 for both nuts. When the technique was applied to the investigation of n = 60 commercial food samples, inconsistencies in labeling some foods were found. Xiao and coworkers [139] reported developing an RT-PCR assay to assess the bovine α-lactalbumin gene. The method was able to detect 0.05 ng of bovine DNA and was successfully tested in n = 42 commercial food samples with or without cow milk listed as an ingredient. Fernandes and coworkers [147] developed a qPCR method to detect shrimp DNA with low levels as one pg. The RT-PCR method focuses on the 16s rRNA mitochondrial gene. Zhang and coworkers [141] developed a qPCR method to test simultaneously for peanut, soybean, and sesame seed in processed foods. Villa and coworkers [140] used RT-PCR to assess milk proteins in meat-based products detecting Foods 2021, 10, 1081 13 of 48 as low as 100 and 50 mg·kg−1 milk protein concentrate in hams/autoclaved sausages and raw sausage mixtures, respectively. Miyazaki and coworkers [144] detected food allergens, including wheat, buckwheat, and peanuts. Their approach, including reference plasmids containing known copies of the target sequences, could cancel inter-instrument variability and avoid risks of false-positives and false-negatives due to trace levels of contaminants from the laboratory environment or agricultural products. The copy numbers of the plasmids were used to detect the allergenic ingredients to 10 mg·protein·kg−1 in highly processed foods. Daems and coworkers developed an alternative to the traditional two-step molecular detection assays (i.e., qPCR followed by a high-resolution melting analysis) applying a fiber optic melting PCR method including mannitol dehydrogenase as a target gene for celery [148]. Interestingly, Suh and coworkers [143] used a multiplex PCR method to assess cod- ing genes from tomato, apple, peach, and kiwi as emerging food allergens. The authors obtained a sensitive method applicable to commercial samples (i.e., juices, dried fruits, and fruit powders). Similar to the other applications mentioned above, commercial kits are available for molecular diagnostics. For example, r-biopharm has a vast catalog of target allergens via qualitative/quantitative PCR (i.e., SureFood® Allergen, using FAM and VIC/HEX fluorescence) that include celery, gluten, soy, almond, Brazil nut, cashew, hazelnut, macadamia, peanut, pecan, pistachio, walnut, crustaceans, fish, mollusks, lupin, mustard, sesame, oat, buckwheat, apricot, and rice. 2.2.6. Beer Spoilage Bacteria Due to its inherent physicochemical characteristics [i.e., low pH (3.8–4.7), ethanol con- centration (0.5–14 g/100 g), and low O2 content/high CO2 (<0.1 mg·L−1 and 0.5 g/100 mL)], beer is quite deterring to bacterial growth, but a few bacterial genera, including Lactobacillus spp., Pediococcus spp., Pectinatus spp., and Megasphaera spp. (the first two genera are re- sponsible for 70 to 90% of spoilage incidents, [103]), can generate off-flavors, turbidity, and acidity [149], causing high economic losses and impact brand image. Species-specific PCR tests targeting the gene sequences encoding rRNA have been evaluated to identify breweries’ contamination [150]. Ma and coworkers [151] applied propidium monoazide prior to PCR analysis, allow- ing live/dead discrimination of bacteria, and used the method to detect beer spoilage bacteria. The intercalating dye covalently binds to the genomic DNA of damaged cell mem- branes, and once in this state, it cannot be amplified. Schneiderbanger and coworkers [152], during a six-year survey using PCR, demonstrated that the most frequent spoilage-related strains are L. brevis (41.9% prevalence), L. backii (related to spoilage with early stages of production), and L. (para-)caseii (late stages and packaging). Similarly, Meier-Dörnberg and coworkers [153] evaluated the incidence of S. cerevisiae var. diastaticus beer contamina- tion in Europe throughout ca. 10 years. About six positive contaminations were detected every year, and contamination events caused 71% of them during the beverages’ filling process. Asano and coworkers [154,155] developed a multiplex PCR method to assess n = 12 beer-related bacteria’s spoilage capabilities. For practical microbiological quality control in breweries, the authors divided the most common spoilage species into three groups without obtaining false-positive results in any of the multiplex assays. Similar to other applications, tailored tests have been designed; for example, Rheonix® Beer SpoilerAlert can detect n = 60 different organisms to help brewers identify spoilage organisms before spoilage occurs. The assay detects over n = 47 different species of lac- tic acid bacteria, n = 7 species of strictly anaerobic bacteria (Megasphaera and Pectinatus), wild yeast (including n = 5 species of Brettanomyces and S. cerevisiae var. diastaticus), and four different hop resistance genes (i.e., horA, horC, bsrA, bsrB). Meanwhile, there is the Invisible sentinel® Veriflow® rapid detection, which requires no DNA purifica- tion, and Pall GeneDisc® Beer Spoilage Bacteria, a kit that permits the simultaneous detection and identification of n = 21 significant beer spoilage microorganisms L. brevis, L. lindneri, L. backii, L. collinoides, L. paracollinoides, L. casei, L. paracasei, L. coryniformis, Foods 2021, 10, 1081 14 of 48 L. rossiae, L. parabuchneri, L. perolens, L. plantarum, P. damnosus, P. inopinatus, P. claussenii, P. cerevisiiphillus, P. frisingensis, P. haikarae, P. portalensis, M. cerevisiae, and M. elsdenii. 2.3. Microwave Plasma-Atomic Emission Spectrometry (MP-AES) Though microwave plasma technology has been available some decades now, it was not until the introduction of commercially available MP-AES equipment that the technique was popularized [156]. MP-AES uses a 2.45 GHz microwave magnetic field to sustain a nitrogen plasma, and recently, several methods have been developed for the elemental analysis of food samples (see Table 3). Table 3. Selected applications in food matrices and parameters for Microwave Plasma-Atomic Emission Spectrometry (MP-AES) analysis. Matrix Sample Treatment Minerals Tested and Wavelengths Used (nm) Sensibility, mg·L−1 or mg·kg−1 Reference Sunflower a Microwave digestion HNO3 Al 394.401/396.152, As 193.695/234.984, Ba 455.403/614.171, Be 234.861, Ca 393.366, 422.673, Cd 226.502/228.802, Co 340.512/345.351, Cr 357.868/425.433, Cu 324.754/327.395, Fe 259.940/ 371.993, K 766.491/769.897, La 394.910, Lu 261.542, Mg 285.213/383.829, Mn 403.076/403.307, Mo 379.825/386.410, Na 588.995/589.592, Ni 341.476/352.454, Pb 368.346/405.781, Sr 407.771/ 460.733 88, Y 371.029, V 309.311/437.923, Zn 213.857/481.053 0.20 × 10−4 [156] Animal Feed Microwave digestion HNO3 Cu 324.754, Fe 259.940, Mn 257.610, and Zn 213.857 1.5 (Mn) to 4.1 (Fe) [157] Aquaculture feed Dry ash Cu, Fe, Mn, Zn, K, and Na 0.4 to 3.9 [158] Biofortified yeast b Methanesulfonic acid digestion 16 h 120 ◦C, heptafluorobutyric acid and K2S2O8 + NaOH/HCl/NaBH4 + NaOH (hydride formation) SeMet and Se 196.026 (3.80 and 7.60) × 10−4 [159] Malbec wines c Dilution HNO3 and ethanol Sr 407.771, Rb 780.027, Mg 279.553, Ca 396.847, Na 589.592, K 769.897 1.0 × 10−3 [160] Bread a Wet digestion HNO3/H2O2 Ca 393.366, Cu 324.754, Fe 371.993, K 766.491, Mg, 285.213, Mn 403.076, P 214.915, Zn 213.857 2.8 × 10−4 (Cu) to 7.5 (P) [161] Cheese a (several varieties) Wet digestion, HNO3 + H2O2, Cs as a suppressor Ca 445.478, K 766.491, Mg 285.213 0.012 (Mg) to 0.19 (Ca) [162] Wines a Standard addition, dilution ethanol B 249.772 0.08 [163] Natural water Filtrate Al 396.152, Cd 228.802, Co 340.512, Cr 425.433, Cu 324.754, Fe 259.940, Mn 403.076, Mg 285.213, Mo 379.825, Ni 352.454, Pb 405.781, and Zn 213.857. 0.005 [164] Californian wines b Dilution HCl/KI (reduction) As 193.695 3.8 × 10−4 [165] Corn a Microwave digestion HNO3 Ag 328.068, Al 396.152, Ba 455.403, Be 234.861, Ca 422.673, Cd 228.802, Co 340.512, Cr 324.433, Cu 324.754, Fe 259.94, Mg 383.829, Mn 403.076, Mo 386.41, Na 589.592, Ni 352.454, Pb 283.305, Tl 535.046, V 437.923, Zn 481.053 0.7 (Mo and Fe) to 4.3 (Ca) [166] Drinking water b None, NaBH4/HCl As 188.979, Se 196.026/203.985, and Hg 0.007 (Hg) to 0.04 (As) Herbal tea infusions Filtrate Cr, Mn, Fe, Ni, Cu, Zn, Cd, Al, Pb, Co 0.01 to 0.12 [167] Henna a Microwave digestion (HNO3/H2O2) Al 396.15 B 249.77, Cd 228.80, Co 340.51, Cr 425.43 Cu 324.75 Fe 371.99, Mn 403.08, Mo 379.83, Ni 352.45, Pb 405.78 Sn 317.51 0.30 (Cu) to 8.43 (Ni) [168] Carob, fig, and almond liquors Wet (HNO3/H2O2/HCl/HClO4) and dry ash mineralization (450 ◦C/HNO3) Na 588.995, K 766.491, Cu 324.754, Ca 393.366, Mg 403.076, Na 588.995, K 766.491, Fe 371.993, Zn 213.857, Mn 403.076, Cd 226.502, Pb 368.346 and P 213.618 0.05 (Cu) to 0.52 (Ca) [169] Salmon b Microwave digestion H2O2 + HNO3/NaBH4 Thiourea Hg 253.652 0.02 [170] Water a Tetrahexylammonium bromide/4- (2-pyridylazo)-resorcinol/ ethanol/HCl Pb 283.305, Cd 228.802, Co 345.351, Ni 305.082, Zn 213.857, and Cu 324.754 0.06 (Cu) to 4.9 (Cd) [171] Goat cheese Dry ash (550 ◦C 5 h and HCl/HNO3) Pb 405.781, As 193.695, Cd 228.802, Al 396.152, Ca 393.366, Mg 285.213, K 766.491, 588.995, Co 340.512, Cu 324.754, Cr 425.433, Fe 371.993, Mn 403.076, Se 196.026, Zn 213.857, Ni 352.454, Sr 407.771 0.023 (Mn) to 100 (Na) [172] Wine a Wet digestion H2O2/HNO3 Mn 403.076 0.67 × 10−4 [173] Honey a Dry ash (450 ◦C for 4 h) and HNO3/H2O2 Cu 324.75, Fe 259.94, Pb 405.78, Zn 213.86, and Cd 228.80 0.29 to 4.5 [174] Coffee, tea, cocoa Dry ash 600 ◦C for 4 h and HNO3 Mn, Cr, Fe, Cu, Zn, Pb, and Cd <0.1 [175] Related matrices Blood b HNO3 + H2O2/KI/NaBH4 + NaOH (hydride formation) Sb 231.147 1.50 × 10−4 [176] a Double-pass glass cyclonic spray chamber. b MSIS spray chamber. c O-ring free glass baffled cyclonic spray chamber. Foods 2021, 10, 1081 15 of 48 2.3.1. Advantages and Limitations of the Application To assess the capabilities of metrology, institutes such as the Inorganic Analysis Working Group (IAWG) of Consultative Committee for Amount of Substance: Metrology in Chemistry and Biology (CCQM) from the Bureau International des Poids et Mesures (BIPM) organized the performance test CCQM-K125 (n = 25 international participants). The inter-laboratory assay was designed to measure trace elements’ mass fractions (K, Cu, and I) in infant formula. Several analytical techniques to assess these minerals were used, including MP-AES [177]. Overall, MP-AES performance offers better detection limits over a broader range of elements than flame atomic absorption spectroscopy (FAAS) and is usually compared to an ICP-AES [167,173]. The applications above demonstrate a great versatility in matrices that can be analyzed and even sensibilities as low as the ng·mL-1, reflecting the possibility of determining even metals in trace concentrations. Another advantage of MP-AES compared to FAAS is that refractive or carbide-forming elements can be more easily determined because plasma temperature is much higher than those of flame atomizers or graphite furnace [160–162]. Despite the virtues concerning FAAS, it still demands the use of only atom lines, additional ionization suppressors (e.g., the addition of CsNO3), and the use of internal standards to resolve spectral interferences [156]. A certain advantage of MP-AES is that results are obtained swiftly (analysis of several minerals simultaneously is possible as the MP-AES can screen different wavelengths in a sequential mode) without requiring hazardous (flammable and oxidizing gases) or expensive gases [using nitrogen plasma instead of the argon plasma (flame temperature 5000 vs. 8000–10,000 K)] [168,175]. Hence, spectral interferences are less far-reaching, and atomic spectral lines have superior clarity. The new MP-AES instruments are mostly equipped with nitrogen generators avoiding manual handling of gas-cylinders in the laboratory [158–160]. However, torches are highly sensitive to deformation (that can occur even due to wrong placement within the instrument chamber) and tubing wear (used to carry the samples with a peristaltic pump) considerably affects the results obtained MP- AES. For example, the accumulation of salts may cause the torch and nebulizer blockage and may break the torch, so total dissolved salt content present in the samples should also be considered [160–162]. As demonstrated through the applications above, the technique can be coupled with a Multimode Sample Introduction System (MSIS) spray chamber for hydride generation that can operate in several modes, which should also be tested for performance for each applica- tion [178]. Finally, in other study fields, the combination of MP-AES and chromatographic techniques has also been exploited (see, as examples, [179,180]). 2.3.2. Selected Applications of MP-AES A work that exemplifies the exploitation of MP-AES’s possibilities was using the technique to analyze n = 23 trace elements in sunflower using La, Lu, and Y as internal standards. Concentrations for trace metals As, Co, and Mo were below the MP-AES detection limit. Additionally, the authors compared the results obtained from MP-AES with those obtained in ICP-MS with a high degree of association [156]. Barrientos and coworkers [159] demonstrated MP-AES’s capacity to be coupled with other techniques such as liquid chromatography. The authors developed a SeMet and Se(IV) determination method in yeast, a commonly used supplement in human and animal nutrition. Ozbek and Akman [163] achieved a very sensitive method to assess minerals in commercial bread samples from Turkish markets, including those made with corn, wheat, and rye. Pascariu and coworkers [164] recorded no heavy metal pollution as they analyzed waters from a pristine source to evaluate if MP-AES was sensitive enough to assess low-level contaminants in creek water (from Piule-Iorgovanul Mountains) and if such water was apt for human consumption according to 98/83/EC. Zaldarraiga Heredia [166] compared traditional microwave-assisted digestion with an ultrasound-assisted one to assess multiple elements in Argentine corn, though MP-AES using In as an internal standard. Rajmund Foods 2021, 10, 1081 16 of 48 and coworkers [167] analyzed n = 31 herbal tea samples, an application relevant since fertilization and environmental pollution will influence these plant materials. Calendula tea showed the highest contamination for Cd and Pb (1.65 and 9.12 mg·kg−1, respectively). Rodríguez-Solana and coworkers [169] tested n = 25 fruit liqueurs from n = 16 Portuguese producers. In carob liqueur, 72.7 and 18.2% of samples showed Fe and Pb, respectively. Almond liqueurs contained the lowest mineral content, with only five elements detected; despite sample variability (probably because of the manufacturing processes), a principal component analysis could differentiate among products. Ozbek [168] analyzed n = 18 henna samples and demonstrated that several samples exceeded international regulatory limits for Pb. Savoie and coworkers [170] used a cold-vapor technique to assess Hg in wild Atlantic salmon; the results obtained were compared with cold-vapor atomic fluorescence spectrometry without observing differences; levels found ranged from 0.15 to 0.29 mg·kg−1 on a dry weight basis. Herman-Lara and coworkers [172] were able to discern, using their mineral profile, among fresh and mature Mexican cheeses and segregate them by geographical origin using the levels for n = 9 minerals. Malhat and coworkers [174] applied the technique to determine heavy and essential minerals in honey and analyzed n = 100 samples of different botanical origin from Egyptian markets. Mean concentrations of Cd, Cu, Fe, Pb, and Zn were found to be 5, 128, 462, 123, and 244 µg·kg−1, respectively. Jung and coworkers [173] compared ICP-OES and MP-AES to assess Mn in Korean wild grape wine. The optimum spectral line (403.076 nm) of MP-AES was different from that (259.373 nm) of ICP-OES, and the authors found a significant matrix effect with the former. The Mn concentration in the wild grape wines was 502–3627 µg·L−1. 2.4. Inductively Coupled Plasma/ICP-MS for Determination Minerals in Honey ICP-MS is yet another example of a multi-elemental analysis technique with a vast analytic work range and is capable of hydrides, traces of Hg, and adds capabilities of a semiquantitative, metal speciation, and isotopic measurement. According to Linge [181], ICP-MS has been considered the most recommended technique for determining chemical elements in honey and pollen (see Table 4 for examples). The elemental fingerprint of honey provides essential information with regard to environmental monitoring, authentication, and quality assurance, including nutritional and toxicological aspects. Table 4. Minerals analyzed and applications of ICP-MS in honey. Location of Origin Sample Treatment Instrument used/Minerals Tested or Isotopes Used Concentrations Found Sensibility Reference µg·L−1 or µg·kg−1 Latvia Wet digestion, dilution/HNO3 + H2O2 Al, As, Ba, Cd, Ce, Co, Cr, Ni, Pb, Rb, Sr, V 1.15 × 103 (Al) to 1.14 × 101 (Pb) <2 [182] China Microwave digestion, HNO3 + H2O2 Agilent 7700x, 23Na, 24Mg, 31P, 39K, 43Ca, 55Mn, 56Fe, 63Cu, 66Zn, 85Rb, 88Sr, and 137Ba 5.52 × 105 (K) to 9.00 × 101 (Cu) 290 (K) to 0.2 (Mn) [183] Argentina Microwave digestion HNO3 Dynamic reaction cell Elan, Perkin Elmer, As, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Se, Tl, U, V, Zn 8.16 × 105 (K) to 3.00 × 101 (Cr) <10 [184] Turkey Microwave digestion HNO3 and HNO3 + H2O2 Agilent 7700x, 24Mg, 27Al, 44Ca, 55Mn, 56Fe, 63Cu, 66Zn, 85Rb, 88Sr, and 7Li, 51V, 52Cr, 59Co, 60Ni, 69Ga, 75As, 78Se, 101Ru, 105Pd, 111Cd, 121Sb, 125Te, 133Cs, 137Ba, 178Hf, 193Ir, 195Pt, 205Tl, 208Pb. 1.20 × 102 (Cu) to 1.25 × 104 (Mg) and 0.6 (Tl) to 42.1 (Cr) <1 (Mn, Cu, Zn, Rb, Sr) to 100 (Ca) and 0.2 (Tl, Sb) to 217 (Li) [185] Romania Microwave digestion HNO3 + H2O2 Agilent 7500, Ag, Al, As, Ba, Be, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Se, Sr, Tl, U, V and Zn 2.29 × 105 (K) to 1 (Tl) 0.25 (Rb) to 118.3 (K) [186] Brazil Microwave digestion, cellulose, NH4NO3, NH4(CO3)2, NH4OH 79Br, 127I 5.81 × 102 to 3.84 × 103 (Br), 9.9 (I) 34 (Br), 6.0 (I) [187] Brazil H2O2 + NH4OH/single reaction chamber system (UltraWaveTM) ELANTM DRC II, Perkin Elmer-SCIEX, 79Br, Cl, and 127I 2.60 × 102 to 1.51 × 103 (Br), 7.00 × 104 to 3.21 × 105 (Cl), 42 (I) <30 (Br) 1.00 × 104 (Cl), and 5 (I) [188] Turkey Microwave digestion HNO3 + H2O2 Agilent 7500 ce, 25Na, 27Al, 39K, 44Ca, 52Cr, 55Mn, 56Fe, 60Ni, 63Cu, 66Zn, 78Se, 111Cd, and 208Pb 4.55 × 104 (K) to 4.56 × 101 (Mn) <1 [189] Foods 2021, 10, 1081 17 of 48 Table 4. Cont. Location of Origin Sample Treatment Instrument used/Minerals Tested or Isotopes Used Concentrations Found Sensibility Reference µg·L−1 or µg·kg−1 Brazil Microwave digestion HNO3 + H2O2 Agilent 7700x, 45Sc, 75As, 79Se, 89Y, 111Cd, 115In, 139La, 140Ce, 141Pr, 144Nd, 150Sm, 152Eu, 157Gd, 162Dy, 165Ho, 167Er, 169Tm, 173Yb, 175Lu, 208Pb, and 209Bi 13.84 (As) to 0.04 (Yb) 1.20 × 10−4 (Tm) to 3.1 (Pb) [190] UAE Microwave digestion, 3 mL/100 mL HNO3 Li, Be, Ag, Cd, Sb, Hg, Tl, Bi, Th, U and Pb, Se, V, Ni, Cr, Al 1 to 25 and 1.00 × 102 to 4.00 × 103 <1 [191] Australia and International Wet digestion HNO3 100 ◦C, 2 h Agilent 7900, Ag, Al, As, Au, B, Ba, Be, Bi, Ca, Cd, Ce, Cs, Cr, Co, Cu, Dy, Er, Eu, Fe, Ga, Gd, Ge, Hg, Hf, Ho, Rb, K, La, Li, Lu, Mg, Mn, Mo, Na, Nb, Nd, Ni, Os, P, Pb, Pd, Pt, Pr, Re, Ru, Se, Sb, Sr, Sm, Sn, Ta, Tb, Te, Th, Tl, Tm, Ti, U, V, W, Y, Yb, Zn, and Zr 1.00 × 106 (Na) to 1.00 × 102 (Sr) Non indicated [192] Serbia Microwave digestion HNO3 + H2O2 As, Cu, Zn, Fe, Cd, and Pb 3 (Cd, As) to 2.21 × 103 (Fe) 1 (As, Cd) to 120 (Zn) [193] Australia Microwave digestion, HNO3 Agilent 8800 Triple quad, Ag, Al, As, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Mo, Na, Ni, P, Pb, Sb, Se, Sn, Sr, V, and Zn 2.5 (Hg) to 9.65 × 105 (K) 5 (As, Hg, Pb) to 5.00 × 103 (P) [194] Romania Microwave digestion HNO3 + H2O2 Al, As, Ba, Ca, Cd, Co, Cr, Cu, Mg, Mn, Na, Ni, K, Pb, Sr, Tl, V, and Zn 1.90 × 105 (K) to 5 (As) 0.50 (Sr, Pb) to 5.00 × 103 (K) [195] 2.4.1. Advantages and Limitations of the Application Though ICP-MS is a considerably expensive technique, analyzing several non-metals using spectrometric techniques is far more efficient than other methods (see, for exam- ple, [196]). Recently, a green chemistry method using ion chromatography, conductimet- ric/mass detection, has been developed specifically for honey [197]. ICP-MS is a very attractive technique, especially in high-throughput laboratories. Hyphenated techniques coupled with ICP-MS such as LC can be used to perform multi-elemental speciation [198]. Furthermore, isotope ratios using ICP-MS have been successfully used to assess geographi- cal origin or agricultural products [199]. However, the acquisition or starting price alone can become a hindrance to some laboratories with restricted budgets. 2.4.2. Selected Applications for ICP-MS Vincevica-Gaile and coworkers [182] analyzed n = 80 honey samples from different varietals. In terms of metal concentrations found in the samples, the authors revealed the following sequence Zn > Al > Cu > Ni > Cr > Pb > Co > Cd > As. Similarly, honey types were ranged, in descending order of contamination, as follows: Commercially manufac- tured honey mixtures with unknown botanical origin > heather/forest blossom honey > multi-floral honey > meadows blossom, honey > linden honey > buckwheat/clover honey > rape/spring blossom honey. Chen and coworkers [183] analyzed n = 163 honey samples, including linden, vitex, rape, and acacia, varietals collected from Heilongjiang, Beijing, Hebei, and Shaanxi, China. The authors used principal component analysis, partial least-squares discriminant analysis, and back-propagation artificial neural network to predict sample botanical origin. Conti and coworkers [184] analyzed mostly multi-floral honey from Buenos Aires. The authors used descriptive statistics, hierarchical cluster, and principal component analysis to classify the samples according to their geographical origin. The authors found K and Na to be quantitatively the most abundant metal (i.e., accounting for 92.5% of total minerals analyzed). Mineral analysis of n = 19 can also be used as a possible approach to evaluate environmental pollution, though minerals such as As, Be, Cd, Co, Cr, Ni, Pb, Se, Tl, U, and V were found below the limit of detection. Döker and coworkers [182] tested the digestion treatment’s impact (n = 4 variants) in the analytical technique sensitivity. Microwave-assisted digestion procedures using diluted reagents emerged as the method of choice for honey. Moreover, n = 14 elements were reported for multi-floral honey. The relatively high concentrations of Al, Cr, and V in honey suggested contamination from metallic equipment during processing. Muller and coworkers [185] Foods 2021, 10, 1081 18 of 48 analyzed three different non-metals (halogens) in honey; their digestion treatment with peroxide produces water in situ that, due to its affinity towards halogens, will hydrate those ions avoiding their loss. Also, the final alkaline pH of digests minimizes memory effects during the ICP-based analysis. Altun and coworkers [189] also determined that honey’s most abundant minerals are K, Na, and Ca. They also used hierarchical clustering to assess the geographical and geochemical precedence of uni and multi-floral honey from n = 8 different Turkish provinces. Ataide de Oliveira and coworkers [190] analyzed n = 21 different trace minerals in honey and pollen. Pillay and coworkers [191] found several interesting things among the honey samples they analyzed. For example, they determined that Li and Be were metals that were relatively stable across the samples. They also found Cd, Hg, and Pb in 7, 5, 133 µg·L−1 each, far below the 1 µg·g−1 values permissible daily intake. As honey samples came from n = 9, different countries used some mineral ratios (i.e., Cr/Tl, Al/Ni, Bi/Ag) to assess the samples’ origin. Hungerford and coworkers [194] analyzed n = 212 samples and found differences among honey from urban, peri-urban, and rural locations. Mineral profiling alone and in conjunction with carbon isotopic ratios have been used to assess honey authenticity [192,195,200]. The above results demonstrate that honey samples’ botanical origin, from mineral profiling, demands a multiplicity of descriptive and inferential statistical and other chemometric analysis [200]. Among the applications mentioned above, some data analysis approaches include linear discrimi- nant analysis, classification and regression trees, artificial neural networking, principal component, and cluster analysis. 2.5. Isotope Ratio Mass Spectrometry (IRMS) Elemental analysis (EA)-IRMS is a bulk measurement technique that provides rep- resentative data for the entire sample’s average isotopic signal [201]. High-temperature pyrolysis (ranging up to 1500 ◦C) has been used as a complement to elemental analysis to assess δ18O and δ2H as well as performing measurements on δ13C in organic samples with a C/O ratio ≈ 1, and δ15N in inorganic samples. Consequently, the technique has been for bulk analyses of stable isotopes on light elements [202]. IRMS has also been used in combi- nation with chromatographic techniques [201]. Application for the food analysis method includes identifying natural/artificial sweeteners and food labeling guarantee, assessing the possible substitution of honey with other sugars, quality control of natural juices and carbonated drinks, and determining aroma profile in apple juices, authentication of organic produce. Additionally, isotope ratios of animal tissue from animals can be determined by their feed source, identity the specific feed, and distinguish whether the production was organic or conventional [201]. A smilar approach, regarding production system, has been for fruits such as tomato [203]. For a primer on principles and instrumentation, we refer the reader toward the recent manuscript by Mai and coworkers [204]. 2.5.1. Advantages and Limitations of the Application IRMS can resolve authenticity issues where traditional physicochemical methods are not capable or are limited to assess adulteration (see, for example, Directive 110/2001/EC for honey). Interestingly, for honey (i.e., isotope ratio for honey/protein), fruit juices, maple syrup, wines and spirits, and vinegar the AOAC, the International Vine and Wine Organiza- tion (OIV), and the European Committee for Standarization (CEN) has already established δ13C and δ18O methods to evaluate authenticity (OMASM 978.17, 998.12, 991.41, 2004.01, TC174 N1048/ENV12140, TC174 N1048/ENV12141, OIV-MA-AS-312-06/OIV OENO 381- 2009, OIV-MA-AS2-12/OIV OENO 381-2009, EN 16466-2:2012, EN 16466-3:2012). However, though EA-IRMS can successfully determine adulteration, the hyphenated techniques (e.g., LC-IRMS in honey markers) have demonstrated a vast improvement in sensitivity and robustness [205]. Though extensive research has been accomplished using this tech- nique, work has to focus on harmonizing these emerging approaches [205]. In some cases, techniques complementary to IRMS will be necessary to determine authenticity successfully. An apparent issue with IRMS is that the instrument’s cost is not trivial (more expensive Foods 2021, 10, 1081 19 of 48 still if a hyphenated technique is desired/required). Maintenance can be cumbersome, as expensive isotopic pure gases will be needed to perform isotope ratio analysis successfully. As demonstrated, simultaneous analysis of several isotopes is possible and suggested, and samples can be determined with minimal or no pretreatment. Initial mass used for IRMS analysis range in the order of µg, hence ensuring sample homogeneity (e.g., siev- ing; small particle, <1 mm) is vital to obtain accurate results. Finally, other isotope-based techniques, including D/H ratio by SNIF-NMR and 1H and 13C NMR fingerprinting, have been widely used to build models for the wine varietal and vintage discrimination [206]. In fact, similar to IRMS, official methods using SNIF-NMR have also been established for authentication of fruit juices (OMASM 995.17), maple syrup (OMASM 2000.19), vanillin (OMASM 2006.05), wines and spirits (OIV-MA-AS311-05 and OIV/OENO 381/2009), and vinegar (EN 16466-1:2012). 2.5.2. Geographical Origin of Coffee, and Honey and Coconut Water Authenticity The stable isotopes composition, such as δ2H, δ13C, δ15N, and δ18O, has been used as markers for environmental conditions and agricultural practices, as well as for the identification of the origin of commodities (Table 5, see for example, [207,208]). Honey is a high-value commodity that is prone to adulteration. The majority of plant species use C3 photosynthesis, in which the first carbon compound produced contains three carbon atoms. However, C4 plants (including maize, sugarcane, and sorghum) avoid photorespiration using another PEP enzyme during the first step of carbon fixation [209]. Hence, honey adulteration with high fructose syrups (a product cheaper than pure honey) obtained from C4 plants are easily identified using IRMS, as they reflect their original carbon isotope composition [192]. In this scenario, bees will be more likely to collect nectar and pollen, for their honey production, from C3 flowers and hence will exhibit a different 13C/12C ratio (or δ13C) than those derived from the C4 pathway [210]. An analogous principle is used during coconut water adulteration with sugar [211]. Table 5. Selected applications for IRMS (coffee and wine geographical origin analysis and the determination of honey and coconut water authenticity). Technique Sample Presentation Number of Samples Countries Isotopes Measured Reference Coffee GC-C-IRMS Roasted coffee 34 Colombia, Brazil, Peru δ13C [212] IRMS (on α-cellulose) Roasted coffee 49 Brazil, Burundi, Colombia, Costa Rica, El Salvador, Ethiopia, Guatemala, Honduras, India, Indonesia, Kenya, Mexico, Nicaragua, Panama, Papua New Guinea, Peru, Rwanda, Tanzania, USA, Vietnam, Yemen δ18O [213] EA-P and EA-C-IRMS Green beans 24 Brazil δ13C, δ18O, δ14N [214] EA-IRMS Roasted coffee 67 Brazil δ13C, δ15N, δ18O, and δ2H [215] GC-C-IRMS Green and roasted coffee 320 Vietnam, Brazil, Cameroon, India, El Salvador, Ethiopia δ13C [216] EA-IRMS Green coffee 81 Ethiopia δ13C, δ15N, and δ18O [217] Wine and related matrices GC-P-IRMS Several wine varietals 110+ Europe δ18O [218] GC-C-IRMS Traditional and Moscatel Sparkling wine 36 Brazil δ13C-CO2 [219] GC-P-IRMS Cabernet Sauvignon, Riesling, Pinot noir, Merlot, Cabernet Gernischet, Chardonnay, Longyan, Crystal and Rose honey 188 China δ18O [220] SNIF-NMR-IRMS Xynisteri, Maratheftiko, Cabernet Sauvignon, Shiraz 76 Cyprus δ2H, δ13C, and δ18O [221] SNIF-NMR-IRMS White (Fiano-Verdicchio) and red (Refosco-Nero) wines 16 Italy δ2H, δ13C and δ18O [222] SNIF-NMR-IRMS Balsamic vinegar must 27 Italy δ2H and δ13C [223] Honey EA-IRMS Raw and commercial 54 Australia, China, India, Indonesia, Iran, South Korea, China, Greece, Hungary, Macedonia, Romania, Serbia, New Zealand δ13C [192] Foods 2021, 10, 1081 20 of 48 Table 5. Cont. Technique Sample Presentation Number of Samples Countries Isotopes Measured Reference EA- and LC-IRMS (on organic acids) Commercial 116 Japan, Spain, France, New Zealand, Italy, China, Hungary, Argentina, Bulgaria, Canada, Mexico, Romania, Taiwan, USA δ13C [224] EA-IRMS Commercial 17 Russia δ13C [225] Coconut water EA-IRMS Commercial/Industrialized 17 Brazil δ13C [211] Coffee IRMS-based analysis includes Arana and coworkers [212], who assessed the carbon ratio of coffee from Colombia and compared it to “fingerprints” from other nearby countries. A valuable effort as will help in the efforts to guarantee the country of origin labeling. Additionally, the authors demonstrated that GC-C-IRMS could be an alternative to NMR when the latter is not available. However, as prediction models are based on the number of variables incorporated into a prediction model, NMR is still advantageous as a vast array of molecules can be measured using the said technique. An evident approach to compensate for the stated limitation is the measurement of isotope ratios of several elements (see [214,215], and previous examples within the paper written by Arana and coworkers [212]). Similarly, Barbosa and coworkers [214] created a prediction model of 91.7% accuracy to foretell Brazilian coffee’s origin using combustion and pyrolysis and com- pared the coffee bean before and after processing (i.e., dehulling/mucilage removal); some relevant differences where observed in the δ14N. The authors also contrasted evaluated coffee sensory descriptors vs. altitude. Although most applications are based on the direct analysis of the untreated samples, Driscoll and coworkers [213] reported a novel approach since they used a spatial model based on δ18O after extracting α-cellulose from the roasted coffee samples. The oxygen isotope ratio of cellulose is a useful geographic tracer, as it integrates climate (e.g., hu- midity, temperature, and precipitation) and source water signals. Similarly, Schipilliti and coworkers [216] used a hyphenated technique to assess isotopic ration in coffee-extracted caffeine. They were able to discriminate based on coffee botanical origin and, in particular, “arabicas” and “robustas”. Peng and coworkers [215] used linear discriminate analysis, k-nearest neighbors, and support vector machines to classify the coffees from several Brazil regions. The authors were able to discriminate organic vs. conventionally cultivated coffee using δ15N. IRMS technique has also been used in conjunction with other analytical ap- proaches such as mineral profiling (e.g., inductively coupled plasma and X-ray fluorescence spectrometry; [217]). Wine isotopic analysis for determining the origin and label verification has been used for 30 years [226]. More recently, dos Santos and coworkers [219] used the carbon isotope ratio to confirm that the isotopic signature of the CO2 could be derived from C4 sugar’s fermentation. Buzek and coworkers [218] demonstrated that the oxygen isotope ratio from a yet to be described source (i.e., Moravian/Czech Republic wines) could be compared to other European wines for which ratios were previously assessed, highlighting the usefulness of the isotopic ration databank/base construction and curation. Bonello and coworkers [222] used two different analytical techniques and sensory analysis. They distinguished two different sets of red and white wine. They discerned among the two distinct regions (i.e., Veneto and Marches, northwest and central Italy, respectively) from which the grapes used to prepare said wines came from. Fan and coworkers [220] used elemental profiling, based on n = 52 elements analyzed by ICP-MS and OES, and oxygen isotope ratio. These variables were incorporated in a multi-step multivariate analysis to verify wines’ origin from three different Chinese regions (i.e., Changji, Changli, and Mile). Perini and coworkers [223] used the δ13C of ethanol, Site-Specific Natural Isotope Fractionation, Nuclear Magnetic Resonance (SNIF-NMR), and detected minor sugars using Ion Chromatography with Pulsed Amperometric and Charged Aerosol Detection. Foods 2021, 10, 1081 21 of 48 In the case of honey, Zhou and coworkers [192] used stable carbon isotopes analysis in honey and its protein to successfully evaluated questionable authenticity. The authors also incorporated trace elements such as Sr, P, Mn, and K into a predictive model to assess geographic origin. Hence, common and prevalent issues of honey authenticity and the mislabeling of its geographic origin can be identified using a combination of the above two techniques. Kawashima and coworkers [224] the δ13C for glucose, fructose, di- and trisaccharides, and organic acids in commercial honey samples employing LC/IRMS and assessed adulterated honey (n = 39, 33.6%), demonstrating yet another approach for IRMS. 2.6. Vibrational Spectrometric (NIR, MIR, FT-IR (Near-, Mid- and Fourier Transform), ATR (Attenuated Total Reflection), Raman Spectroscopy) 2.6.1. Advantages and Limitations of the Application Two advantages of these techniques include the price tag of a NIR, MIR, or FTIR system is not as expensive as other techniques listed here. The maintenance required is mostly based on the upkeep of the optic system. The samples require minimal to no treatment, generate no waste or contaminants, determination, and data acquisition is very fast, and the measurement per se is highly user-friendly. As in other examples, the strenuous part of the work will be centered on chemometric analysis needed to analyze data and how vibrational bands can be useful for an individual application. Although other techniques can be used as a possible approach to detect adulteration, the less laborious and time-consuming alternative methods, based on vibrational spectroscopy, in the majority of cases associated with chemometric tools, have been developed. Vibrational approaches benefit from being non-destructive and considered green chemistry, while several markers can be selected for predictive model construction. Other technical approaches to assess juice and pulp adulteration include isotope ratio (SNIF-NMR and IRMS), NMR fingerprinting, and RT-PCR to determine juice authentic- ity [227]. Furthermore, the mineral profile using ICP-MS has been used to determine the geographical origin and distinguish between conventional and organically grown produce, similar to the applications mentioned herein [227]. In the case of meat authenticity, histo- logical techniques have been developed to determine the disruption of the normal muscle tissue [228,229]. However, this approach requires particular (technically skilled) personnel with specialized knowledge on how to process and interpret histological findings and data and probably even a veterinary and pathology background. Other approaches include DNA fingerprinting, RT-PCR, and its variants, GC, LC/HPLC, ELISA, immunoblotting, and electrophoretic analysis [230]. Each has its own set of limitations, including costs, laboriousness, appropriateness, time-consumption, a diverse range of equipment, and difficulty interpreting obtained results [231]. For an excellent discussion regarding NIR applications in meat processing, we refer the reader to the review written by Dixit and coworkers [232]. As discussed above, research-wise feedstuff analysis using NIR has great potential. NIR applications to assess the quality of feed protein ingredients have been reviewed in the past [233]. However, in terms of feed analysis, for AAFCO Check Sample 2020-28 Cattle Feed, the protein analysis n = 118 laboratories used using combustion analysis (AOAC OMASM method 990.03, AOCS method Ba4e-93) while only n = 5 used NIR. Hence, in this scenario, legal decision-making (e.g., label guarantee analysis in official feed laboratories) from data determined by NIR should be restrained at best. Notwithstanding, due to its rapidness, one cannot negate the usefulness of the tool for on-site or in situ analysis and screening. Commercial handheld NIR equipment is available and has been scientifically evaluated for real-time analysis and field monitoring [234,235]. Additionally, good calibra- tion required for quantitative accurate chemical analysis requires is laborious as requires multiple analysis by standard and NIR methods simultaneously. Finally, Raman spec- troscopy and hyperspectral imaging have found further food research applications, such as the screening of adulterants in cereals or as a non-destructive approach for the nutritional quality of fruits, determination of antimicrobial drugs, mycotoxins and mycotoxigenic fungi (see, for example, [236–240]). Foods 2021, 10, 1081 22 of 48 2.6.2. Detection of Adulterants in Meat and Juices Several spectroscopy techniques based on molecule vibrational energy have been used to assess adulterants and contaminants [227]. This is especially true for fruit juices included in several listings as the foremost food products at risk of fraud [241]. In meat, authentication problems can be categorized into four major areas where fraud is most likely to occur, i.e., meat origin, meat substitution, meat processing, and non-meat ingredient addition [242]. Thus, adulterations involving bovine, pork, horse, turkey, and llama meats have been detected using different spectroscopic techniques with or without data fusion (e.g., Raman, NIR, MIR; [243–246]). The most recent examples of vibrational spectroscopy analysis for adulteration of juices, pulps, and meat tissue can be found in Table 6. Table 6. Selected applications for vibrational spectroscopy used for juice and meat authenticity. Matrix Adulterant Technique Markers Used Reference Fruit juices, nectars, or pulps Concord Grape Grape juice blends FT-IR Phenolic compound-rich fraction [247] Orange Added sugar FT-IR Whole spectra [248] Passion fruit and guava Water NIR Whole spectra [249] Orange Added sugar FT-IR Fructose, glucose, and sucrose [250] Orange Concentrate vs. fresh squeezed ATR-FTIR Whole spectra [251] Grape Apple and cashew juice MIR/ATR-0FTIR Whole spectra [70,71] Grape, orange, peach, and passion fruit Syrup, apple, cashew MIR/ATR-FTIR Whole spectra [72] Guava Sugar and water NIR and MIR Whole spectra [252] Spirit drinks Distilled and aged ethanol Counterfeit alcohol/ denaturants and additives Raman C-C stretch at 892, C-O stretch at 1059 and 1097, and CHx bend at 1460 cm−1 [253] Natural drinks Coconut water Sugar and water Raman Fructose, glucose, and sucrose at 627, 835, and 1123 cm−1 [254] Meat tissue Bovine NaCl, phosphates, carrageenan, maltodextrin ATR-FTIR Whole spectra [255] Bovine Salts and carrageenan Raman Whole spectra [256] Salmon Water NIR Whole spectra [257] Beef and mutton Pork FT-IR Whole spectra [258] As Concord grapes (a cultivar derived from the grape species Vitis labrusca L.) have been associated with health benefits, Snyder and coworkers [247] gave themselves to the task to assess the content of Concord grapes in grape juice blends. Nawayon and cowork- ers [248] prepared n = 80 with different sugar additions and could discriminate them using NIR. Alamar and coworkers [249] used NIR analysis as an alternative to analytical methods currently used to evaluate the quality (moisture, total sugars, acidity, soluble solids, pH, and ascorbic acid) of frozen guava and passion fruit pulps. Ellis and coworkers [250] did discriminate diluted juices using FT-IR and the GC-MS analysis of fructose, glucose, and sucrose. Shen and coworkers [251] used both ATR-FTIR and an electronic nose (aromas and volatiles) and GC-MS (flavor profiles) to assess freshly squeezed juice and adulterated juices. A handheld device has also been adapted for in situ monitoring markers of coun- terfeit alcohol through the container without sample manipulation [253]. Richardson and coworkers [254] used Raman spectroscopy to assess Costa Rican coconut waters’ adulter- ation by dilution with water and single sugars, mixtures of sugars, and high-fructose corn syrup after pasteurization. Finally, Alamar and coworkers [252] prepared pulps in a pilot plant, and fresh and adulterated samples with sugars and water were assessed using NIR and MIR. It was possible to differentiate adulterated from authentic samples, except for water-adulterated samples using NIR spectra. Nunes and coworkers [255,256] evaluated non-meat ingredients to increase meat’s water holding capacity in the case of meat tissue. Protein, ash, chloride, sodium, phos- phate, and Raman (i.e., 1800 to 700 cm−1) and ATR-FTIR spectra were used to determine bovine meat adulteration. Zhang and coworkers [257] used hyperspectral images (i.e., hy- perspectral imaging technology can derive a large amount of imaging information at Foods 2021, 10, 1081 23 of 48 continuous spectral wavelengths and varied spatial dimensions) and near-infrared spectral information of water-adulterated salmon. Yang and coworkers [258] spectrally verified if beef and mutton meat was adulterated with pork meat. A common adulteration practice found commercially since pork meats are considerably less expensive than bovine or lamb meat cuts. 2.6.3. Feedstuff Analysis Using NIR Aureli and coworkers [259] developed a calibration to predict protein, total P, and phytate-bind P in feed ingredients used for monogastric animal diets. At least n = 14 plant ingredients (represented by n = 557 samples) were assayed, including cereals and oilseed meals with SEP values of 9.06, 0.80, and 0.66 g·kg−1 for each analyte, respectively. Whilst the RMSEP measures the accuracy of prediction, the SEP measures the precision of the projection (i.e., the difference between repeated measurements). Similarly, Fan and cowork- ers [260] used n = 829 samples to assess protein content from several feed ingredients from China’s markets. Ferrerira and coworkers [261] determined dry matter, acid and neutral detergent fiber (ADF and NDF), gross energy, crude fat, ash, and protein to validate a mathematical model then to assess the metabolizable energy of corn (n = 99 samples) used in swine feed. Crude ash, fat, and NDF were the variables with the most significant weight. Once the calibration and model are performed, metabolizable energy estimation can be determined swiftly (in a matter of minutes). In contrast, through standard methods, the same assessment would take days or even weeks as it takes considerably longer to get all data from wet chemistry analysis. Metabolizable energy is a useful indicator to assess the animal nutritional requirements assuming losses due to metabolic processes. Alternatively, it will require live animals and a marker (iron oxide is usually used as a fecal marker) to perform metabolism assays. In vitro, dry and organic matter digestibility, and neutral and acid detergent fiber (both indicators useful to assess feed quality) were successfully evalu- ated by Samadi and coworkers [262]. The digestibility assays’ wet chemistry counterparts usually require two individual 48 h steps for fermentation and enzymatic digestion. NIR also permits rapid and inexpensive predictions of the nutritional characteristics of forages consumed mainly by ruminants. Karayilanli and coworkers [263] determined the botanical composition of alfalfa and grass mixtures of fresh or ensiled forage; ration consis- tency and quality based on high-quality forage are paramount to dairy cow productivity. Andueza and coworkers [264] calibrated using n = 1040 samples of feces of temperate forage-fed sheep to assess fecal crude ash, protein, fresh forage organic matter digestibility, and voluntary intake. The authors concluded that species-specific calibration models out- perform other strategies but entail outstanding maintenance and sample numbers. They also suggest that variability in the predicting model can be broadened using forages from other latitudes, quality, or season. Dry matter determinations for alfalfa, and corn silages obtained with NIR have been compared with those acquired by oven drying and other on-farm methods getting differences as high as 3.5 units [234]. Parrini and coworkers [265] and Yang and coworkers [230] have successfully determined the quality parameters of Italian and Chinese (i.e., Lolium multiflorum Lam.) forages. Also, despite broad variability in the plants’ taxonomy and maturity, an Australian research group was able to assess in more than 100 annual and perennial forage species NDF, ADF, pepsin-cellulase dry matter digestibility, and even predicted CH4 production during batch culture fermentation [231]. The authors obtained acceptable RPDs (i.e., the ratio of the standard deviation of the reference data is ≥3). 2.7. Accelerated Oxidation Accelerated oxidation tests using OXITEST have now ten years in the market [266]. We selected recent applications using OXITEST® (VELP Scientifica, Usmate Velate, Italy, AOCS Cd 12c-16) as it has demonstrated to be a more accessible, faster, and greener alternative to the RANCIMAT® method (Metrohm, Switzerland, AOCS Cd 12-57) [267]. Most food research using the instrument focuses on the shelf life and antioxidant potential of food Foods 2021, 10, 1081 24 of 48 products with and without additives (Table 7). Generally, the data obtained from the OXITEST® reactor is expressed as an induction period (IP). Samples are subjected to an environment with high temperature (90 ◦C) and oxygen pressure (6 atm). Hence, values elevate as the sample is more resistant to oxidation. Table 7. Selected examples of OXITEST® to assess food shelf life. Country Matrix Tested Application Induction Period (h) or Shelf Life and Temperature (◦C, days) Reference Italy Chia seeds Differential analysis by country 13.01 [268] Brazil Rosemary leaves emulsion Antioxidant capabilities 24.45 [269] Italy Bakery snack/Tarallini Shelf life 20.90 (using extra virgin olive oil) [270] Kosovo Cured and fresh meats Effect of the addition of nitrates and chili peppers 15.11 (sausage with onions and peppers) [271] Italy Tarallini Enrichment with Tyrosyl oleate 25.28 [272] China Steam pork belly Enrichment with pickled and dried mustard/Nutritional quality Days [273] Thailand Animal feed Addition of tamarind polyphenols 5.42/5.43 [274] Italy Walnut paste Enrichment with grape skin extract 13.85 [275] Turkey Sunflower oil Effects of surfactants on emulsions 13.42 [276] Russia Oregano extract Antioxidant capabilities 0.52 [277] Italy Sunflower oil Enrichment with polyphenols from olive mill wastewater 17.03 [278] China God’s flower Potential oil source Days [279] 2.7.1. Advantages and Limitations of the Application OXITEST® technology offers rapid information regarding food shelf life that, if not available, would turn the data gathering to be expensive, cumbersome, and somewhat slow; for example, alternatives will require sensory expert panels for rancid perception (see, for instance, [280]. Additionally, The OXITEST® allows measuring the modification of absolute pressure inside the two chambers and, through the OXISoft® Software, automatically generates the IP expressed as hours by the graphical method [280]. Another attractive feature of OXITEST® lies in that accelerated oxidation of whole, untreated samples (i.e., as is or without the previous extraction of fat) can be performed (see, for example, [268]). 2.7.2. Selected Applications for OXITEST® Claus and coworkers [269] combined OXITEST® with hydro- and lipophilic oxygen radical absorbance capacity results and demonstrated that rosemary (Rosmarinus officinalis L.) is a promising candidate as a canola oil protectant. Karadag [276] prepared different sunflower oil emulsions in water-mediated by sunflower lecithin, whey protein isolate, whey protein concentrate, citrus pectin, Tween 80, and the mixture of Tween 80 and Span 20 at two different pH (i.e., 4 and 7). The author demonstrated that lecithin and pectin showed the highest and lowest induction periods’ values, respectively, at both pH values. These results are relevant since many food formulations can be classified as emulsions, and is in this interface that oxidation occurs. Morina and coworkers [271] demonstrated that fresh calf meat (i.e., three hours after slaughter) showed a high IP while prepared meat (possibly due to manipulation) exhibited the lowest values. Meanwhile, blended meats and sausage containing peppers and onions revealed the overall highest IP values. Riciputi and Caboni [270] compared the Italian bakery product’s IP when prepared using different cooking oils. They demonstrated that IP was improved when the preparation was made using extra virgin olive oil instead of sunflower oil. They also showed that coarse milling of ingredients increased significantly the IP, a particularly relevant result that can be considered during baked goods formulation to improve shelf life. However, coarse grinding can have a negative effect on palatability. Marzocchi and Carboni [272] synthesized tyrosyl oleate and incorporated it into the for- mulation for tarallini, which improved the IP four-fold compared to the control sample (from 6.10 to 25.28 h using 7 g/100 g oleate). The authors also monitored the baked goods’ volatile profile that resulted from the Maillard reaction, caramelization, and lipid oxidation using an SPME-GC/MS method. Shan and coworkers [273] demonstrated that steaming pork has a detrimental impact on IP values compared to raw meat. Simultaneously, the Foods 2021, 10, 1081 25 of 48 addition of 40 g/100 g pickled dried mustard increased this value ca. two-fold, without affecting the sensory quality of the meat. Thanomwongwatana [274] added a polyphenol- rich extract from Tamarindus indica L. seed husks to feed. Extracts at 0.5 and 1.0 mL/100 g had the highest antioxidation potentials, with the average IP readings at 5.42 and 5.43 h, respectively. Grape skin extract in concentrations of 5 000 mg kg−1 was also used to im- prove walnut paste’s IP values [275]. Oleynikov [277] demonstrated that beef prepared with oregano extract exhibited higher IP values than beef formulated with additives E262, E300, E301, E331, and E391, which means that oregano extract was better equipped to slow down myoglobin oxidation in meat. Similarly, Romeo and coworkers [278] demonstrated that polyphenols from olive mill wastewaters used to enrich sunflower oil improved its resistance considerably to oxidation by 50% (IP of 1 022 min) with respect to the control IP of about 540 min. Several examples demonstrate the use of natural antioxidants to improve food properties, but they establish a vast and relatively untapped potential to exploit agro by-products. Amato and coworkers [268] compared the nutritional quality of chia seeds produced from Italy’s southern