Amid today’s stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

Meat and fish chemical composition and sensory attributes are markers of quality that require innovative assessment methods as existing ones are rather technical, laborious, and expensive. Emerging trends of advanced technology instruments have been lauded in the pharmaceutical, cosmetic and food industries for their high sensitivity, customizability, rapidness and affordability. Common among these, are the electronic tongue (e-tongue) and electronic nose (e-nose) but their use for meat and fish quality, remains scanty and scattered. This paper aims to systematically discuss the developing trends, principles and the recent use of e-tongue and e-nose for quality measurements in fish and meat. From over 90 research papers, it was observed that an arsenal of chemometric tools have been pivotal in applying these instruments for rapid quantitative, qualitative and predictive analysis of some physical properties, chemical properties, storability and the authentication of meat and fish. Both instruments require no reagent (waste free analytical procedure) and have been lauded for precision and*accuracy but e-nose may be better suited for meat and fish assessments. Unlike the e-tongue, e-nose requires no liquid sample preparation and portable versions are promising for rapid remote analysis of meat and fish samples that can save cost on transferring carcass to laboratories.

Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, ¹H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.

Tokaj wines (Hungaricum) are botrytized wines acknowledged for the unique organoleptic properties bestowed by botrytized grape berries during production. Excluding these berries during wine production or manipulating the sugar content of low‐grade wines to imitate high‐grade wines are some recent suspicious activities that threaten the wine quality. Advanced methods such as spectroscopy and sensor‐based devices have been lauded for rapid, reliable, and cost‐effective analysis, but there has been no report of their application to monitor grape must concentrate adulteration in botrytized wines. The study aimed to develop models to rapidly discriminate lower grade Tokaj wines, “Forditas I” and “Forditas II,” that were artificially adulterated with grape must concentrate to match the sugar content of high‐grade Tokaj wines using an electronic tongue (e‐tongue) and two near infrared spectrometers (NIRS). Data were evaluated with the following chemometrics: principal component analysis (PCA), discriminant analysis (LDA), partial least square regression (PLSR), and aquaphotomics (a novel approach). There was a noticeable pattern of separation in PCA for all three instruments and 100% classification of adulterated and nonadulterated wines in LDA using the e‐tongue. Aquagrams from the aquaphotomics approach showed important water absorption bands capable of being markers of Tokaj wine quality. PLSR models showed coefficient of determination ( R ² CV) of 0.98 (e‐tongue), 0.97 (benchtop NIRS), 0.87 (handheld NIRS), and low root mean squared errors of cross‐validation. All three instruments could discriminate, classify, and predict grape must concentrate adulteration in Tokaj with a high accuracy and low error. The methods can be applied for routine quality checks of botrytized wines. Practical Application: Tokaj wines (Hungaricum) are botrytized wines acknowledged for the unique organoleptic properties bestowed by botrytized grape berries during production. Excluding these berries during wine production or manipulating the sugar content of low‐grade wines to imitate high‐grade wines are some recent suspicious activities that threaten the wine quality. Using advanced instruments, the electronic tongue, benchtop near infrared spectroscopy, and a handheld near infrared spectroscopy, we could discriminate, classify, and predict grape must concentrate adulteration in Tokaj with a high accuracy and low error. The models in our study can be applied for routine quality checks of botrytized wines.

Temperature, memory effect, and cross-contamination are suspected to contribute to drift in electronic tongue (e-tongue) sensors, therefore drift corrections are required. This paper aimed to assess the disturbing effects on the sensor signals during measurement with an Alpha Astree e-tongue and to develop drift correction techniques. Apple juice samples were measured at different temperatures. pH change of apple juice samples was measured to assess cross-contamination. Different sequential orders of model solutions and apple juice samples were applied to evaluate the memory effect. Model solutions corresponding to basic tastes and commercial apple juice samples were measured for six consecutive weeks to model drift of the sensor signals. Result showed that temperature, cross-contamination, and memory effect influenced the sensor signals. Three drift correction methods: additive drift correction based on all samples, additive drift correction based on reference samples, and multi sensor linear correction, were developed and compared to the component correction in literature through linear discriminant analysis (LDA). LDA analysis showed all the four methods were effective in reducing sensor drift in long-term measurements but the additive correction relative to the whole sample set gave the best results. The results could be explored for long-term measurements with the e-tongue.

The chemical composition of bee pollens differs greatly and depends primarily on the botanical origin of the product. Therefore, it is a crucially important task to discriminate pollens of different plant species. In our work, we aim to determine the applicability of microscopic pollen analysis, spectral colour measurement, sensory, NIR spectroscopy, e-nose and e-tongue methods for the classification of bee pollen of five different botanical origins. Chemometric methods (PCA, LDA) were used to classify bee pollen loads by analysing the statistical pattern of the samples and to determine the independent and combined effects of the above-mentioned methods. The results of the microscopic analysis identified 100% of sunflower, red clover, rapeseed and two polyfloral pollens mainly containing lakeshore bulrush and spiny plumeless thistle. The colour profiles of the samples were different for the five different samples. E-nose and NIR provided 100% classification accuracy, while e-tongue > 94% classification accuracy for the botanical origin identification using LDA. Partial least square regression (PLS) results built to regress on the sensory and spectral colour attributes using the fused data of NIR spectroscopy, e-nose and e-tongue showed higher than 0.8 R² during the validation except for one attribute, which was much higher compared to the independent models built for instruments.