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.

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.

Tomato, and its concentrate are important food ingredients with outstanding gastronomic and industrial importance due to their unique organoleptic, dietary, and compositional properties. Various forms of food adulteration are often suspected in the different tomato-based products causing major economic and sometimes even health problems for the farmers, food industry and consumers. Near infrared (NIR) spectroscopy and electronic tongue (e-tongue) have been lauded as advanced, high sensitivity techniques for quality control. The aim of the present research was to detect and predict relatively low concentration of adulterants, such as paprika seed and corn starch (0.5, 1, 2, 5, 10%), sucrose and salt (0.5, 1, 2, 5%), in tomato paste using conventional (soluble solid content, consistency) and advanced analytical techniques (NIR spectroscopy, e-tongue). The results obtained with the conventional methods were analyzed with univariate statistics (ANOVA), while the data obtained with advanced analytical methods were analyzed with multivariate methods (Principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares regression (PLSR). The conventional methods were only able to detect adulteration at higher concentrations (5–10%). For NIRS and e-tongue, good accuracies were obtained, even in identifying minimal adulterant concentrations (0.5%). Comparatively, NIR spectroscopy proved to be easier to implement and more accurate during our evaluations, when the adulterant contents were estimated with R2 above 0.96 and root mean square error (RMSE) below 1%.

Paprika powder is a spice of culinary importance in many homes but it's powdered form, has been targeted for fraudulent activities intended at consumer deception. Diverse methods have been used to investigate some of these adulterations but there is no report of paprika adulteration with corn flour, although it remains a suspicion. Technologies such as the near infrared spectroscopy (NIRS) possess non-invasive and rapid advantages that could be explored to monitor this type of adulteration. The study aimed to discriminate and quantify different levels of paprika powder adulterated with corn flour, using NIRS. Two authentic paprika types (DP and SP) were purchased from reputable sources in Hungary and artificially adulterated in the laboratory. Three repeats of each adulteration level (40%, 30%, 25%, 20%, 15%, 10%, 5%, 3%, 1%) were prepared and scanned with the Metri NIRS respectively, then, analysed with chemometrics: Linear discriminant analysis (LDA) and partial least squares regression (PLSR). LDA showed 100% recognition and prediction accuracies respectively when DP and SP were analyzed separately to discriminate different concentrations of paprika adulteration. LDA models with NIRS recognize corn flour adulteration with 95.55% and predict it with 95.02% accuracy irrespective of the paprika type used in this experiment. PLSR prediction of 40%, 30%, 25%, 20%, 15%, 10%, 5%, 3%, 1% corn flour adulteration yielded an R 2 CV of 0.98 (high accuracy) and a low RMSECV of 1.71 g/100g (low error). Near infrared as a non-invasive technique exhibited good potentials for paprika powder authentication when corn flour is used as an adulterant.

No abstract

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R2CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio.

Mung bean is a leguminous crop with specific trait in its diet, namely in the form of anti-nutrient components. The sprouting process is commonly done for better nutritional acceptance of mung bean as it presents better nutritional benefits. Sprouted mung bean serves as a cheap source of protein and ascorbic acid, which are dependent on the sprouting process, hence the importance of following the biological process. In larger production scale, there has not been a definite standard for mung bean sprouting, raising the need for quick and effective mung bean sprout quality checks. In this regard, near-infrared spectroscopy (NIRS) has been recognized as a highly sensitive technique for quality control that seems suitable for this study. The aim of this paper was to describe quality parameters (water content, pH, conductivity, and ascorbic acid by titration) during sprouting using conventional analytical methods and advanced NIRS techniques as correlative methods for modelling sprouted mung beans’ quality and ascorbic acid content. Mung beans were sprouted in 6 h intervals up to 120 h and analyzed using conventional methods and a NIR instrument. The results of the standard analytical methods were analyzed with univariate statistics (analysis of variance (ANOVA)), and the NIRS spectral data was assessed with the chemometrics approach (principal component analysis (PCA), discriminant analysis (DA), and partial least squares regression (PLSR)). Water content showed a monotonous increase during the 120 h of sprouting. The change in pH and conductivity did not describe a clear pattern during the sprouting, confirming the complexity of the biological process. Spectral data-based discriminant analysis was able to distinctly classify the bean sprouts with 100% prediction accuracy. A NIRS-based model for ascorbic acid determination was made using standard ascorbic acid to quantify the components in the bean extract. A rapid detection technique within sub-percent level was developed for mung bean ascorbic acid content with R2 above 0.90. The NIR-based prediction offers reliable estimation of mung bean sprout quality

Nitrogen-rich adulterants in protein powders present sensitivity challenges to conventional combustion methods of protein determination which can be overcome by near Infrared spectroscopy (NIRS). NIRS is a rapid analytical method with high sensitivity and non-invasive advantages. This study developed robust models using benchtop and handheld spectrometers to predict low concentrations of urea, glycine, taurine, and melamine in whey protein powder (WPP). Effectiveness of scanning samples through optical glass and polyethylene bags was also tested for the handheld NIRS. WPP was adulterated up to six concentration levels from 0.5% to 3% w/w. The two spectrometers were used to obtain three datasets of 819 diffuse reflectance spectra each that were pretreated before linear discriminant analysis (LDA) and regression (PLSR). Pretreatment was effective and revealed important absorption bands that could be correlated with the chemical properties of the mixtures. Benchtop NIR spectrometer showed the best results in LDA and PLSR but handheld NIR spectrometers showed comparatively good results. There were high prediction accuracies and low errors attesting to the robustness of the developed PLSR models using independent test set validation. Both the plastic bag and optical glass gave good results with accuracies depending on the adulterant of interest and can be used for field applications.