Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

Chemometrics-powered spectroscopic techniques for the measurement of food-derived phenolics and vitamins in foods: A review

Foods are rich in various bioactive compounds, like phenolics, and vitamins, which play important physiological roles in the human body. The analysis of phenolics and vitamins in plant and animal-based foods is a topic of growing interest. Compared with conventional methods, the chemometrics-powered infrared, Fourier transform-near infrared and mid-infrared, ultraviolet-visible, fluorescence, and Raman spectroscopy offer a reliable, low-cost, and nondestructive means to determine phenolics and vitamins. This study briefly presents the physical properties of phenolics and vitamins and their physiological benefits, features of commonly used spectroscopic techniques, sample preparation for spectroscopic data analysis, and the progress of chemometrics methods for model calibration using spectroscopic data and their primary challenges in predicting phenolics and vitamins in real samples for the last five years. The spectral preprocessing method combined feature extraction quantitative chemometric model comparatively showed the best results for simultaneous and single detection. Finally, this study put forward future directions.

A novel quantum dot-based ratiometric fluorescence sensor array: For reducing substances detection and Baijiu quality discrimination

BACKGROUND

Discriminating the quality of baijiu is critical for fostering the growth of the China baijiu market and safeguarding customers' rights. However, establishing a small-scale and rapid baijiu discriminating sensor assay still remains a challenge.

RESULTS

Here, we first introduced ratiometric fluorescence sensor array for the detection of reducing substances in baijiu to achieve baijiu discrimination. A ratiometric fluorescence sensor array is built using 2,3-diaminophenazine (oxidized-state OPD, oxOPD) to quench three distinct fluorescence signals of quantum dots while reducing interference from background signals. The reducing chemicals in baijiu can react with Ag+, weakening the quenching effect and changing the ratio. The discriminating of 12 types of organic small molecules which were presented in baijiu was achieved with 97.2 % accuracy by using machine learning classification methods. Meanwhile, 0.1 μM limit of detection (LOD) for ascorbic acid shows that our methods have the potential to quantitative detect reducing substances. In real sample detection, our methods can discriminate 10 distinct qualities of baijiu with 100 % accuracy. We also encoded the fingerprints of different varieties of baijiu for quality control and information reading.

SIGNIFICANCE AND NOVELTY

Overall, our easy but robust sensing array not only overcomes the problem of background signal interference but also gives an ideal way for discriminating different qualities of baijiu, food and other areas.

Recent advances on artificial intelligence-based approaches for food adulteration and fraud detection in the food industry: Challenges and opportunities

Food adulteration is the deceitful practice of misleading consumers about food to profit from it. The threat to public health and food quality or nutritional valuable make it a major issue. Food origin and adulteration should be considered to safeguard customers against fraud. It has been established that artificial intelligence is a cutting-edge technology in food science and engineering. In this study, it has been explained how AI detects food tampering. Applications of AI such as machine learning tools in food quality have been studied. This review covered several food quality detection web-based information sources. The methods used to detect food adulteration and food quality standards have been highlighted. Various comparisons between state-of-the-art techniques, datasets, and outcomes have been conducted. The outcomes of this investigation will assist researchers choose the best food quality method. It will help them identify of foods that have been explored by researchers and potential research avenues.

A comprehensive review of analytical techniques for spice quality and safety assessment in the modern food industry

The rapid evolution of analytical techniques has revolutionized spice quality assessment, offering unprecedented opportunities for ensuring the safety, authenticity, and desirable characteristics of these valuable commodities. This review explores the innovative integration of advanced chromatographic, spectroscopic, sensory and molecular methods, combined with chemometrics and machine learning, to provide a comprehensive and standardized approach to spice analysis. The development of portable, rapid screening devices and the application of cutting-edge technologies, such as hyperspectral imaging, molecular and electronic sensory technologies, and their combined applications with other technologies are discussed in the review. Furthermore, the review emphasizes the importance of global collaboration and the shift toward non-targeted approaches to detect novel adulterants and contaminants. By adopting these innovative strategies, the modern food industry can effectively address the challenges of spice quality control, safeguarding consumer health and maintaining the integrity of these essential ingredients in the global culinary landscape.

Excitation-emission matrix spectroscopy coupled with chemometrics for monitoring ozonation of olive oil and olive pomace oil

The effects of ozonation on the Total Polyphenol Content (TPC) of olive oils remain largely unexplored, despite the significant role that polyphenols play in enhancing the health benefits and quality of these oils. Understanding how ozone treatment impacts phenolic compounds is vital, especially considering the documented negative effects of thermal and photochemical oxidation on TPC. The aim of this study was to explore the use of fluorescence spectroscopy combined with chemometrics to develop multivariate models for monitoring the effects of ozonation on TPC and key physicochemical parameters such as the peroxide index (PI), acidity index (AI), iodine value (IV) and viscosity (V) in both, virgin and pomace olive oils. Parallel factor analysis and principal component analysis of fluorescence excitation-emission matrices (EEMs) of ozonated olive oils revealed that as the ozonation process progressed, TPC and fluorescence emission decreased. And, at the same time, ozonation increased the values of oxidation indicators such as PI, AI, viscosity and intensity of the Rayleigh scattering signal. PLS models based on analysis of unfolded EEMs exhibited good predictive performance for PI (R2 = 0.822; RPD > 2.5), and moderate for TPC and V (R2 = 0.792 and 0.753; RPD > 2). In summary, we demonstrated the feasibility of EEM spectroscopy for monitoring the ozonation process. The use of this method can ease the characterization of ozonated olive oils and, additionally, make the analysis more sustainable.

Rapid qualitative and quantitative detection for adulteration of Atractylodis Rhizoma using hyperspectral imaging combined with chemometric methods

In the field of traditional Chinese medicine, Atractylodis Rhizoma (AR) is commonly used for various diseases due to its excellent ability to dry dampness and strengthen the spleen, especially popular in East Asia. The aim of this study is to proposed Hyperspectral Imaging (HSI) in combination with chemometric methods for the rapid qualitative and quantitative detection of AR adulteration with other types of powder. Partial Least Squares Discriminant Analysis (PLS-DA) was used to construct the classification models the best, with the First-order Derivative (F-D) preprocessing method. The accuracy values of the test sets for classification models were above 99%. Furthermore, Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and BP Neural Network (BPNN) were used to quantitatively analyze the adulteration level. On the whole, the BPNN model has a relatively stable effect. The R-square (R2) values of different models were all greater than 0.97, the Root Mean Square Error (RMSE) values were all less than 0.0300, and the Relative Percentage Difference (RPD) values were over 6.00. After applying three characteristic wavelength selection algorithms, namely Iterative Retained Information Variable (IRIV), Successive Projections Algorithm (SPA), and Variable Iterative Space Shrinkage Approach (VISSA) algorithms, the classification accuracy values remained over 99.00% while the quantification models' RPD values were over 4.00. These results demonstrate the reliability of using hyperspectral imaging combined with chemometrics methods for the adulteration problems in AR.

Advancing food security through drone-based hyperspectral imaging: applications in precision agriculture and post-harvest management

Ensuring global food security in the face of growing population, climate change, and resource limitations is a critical challenge. Hyperspectral imaging (HSI), particularly when combined with drone technology, offers innovative solutions to enhance agricultural productivity and food quality by providing detailed, real-time data on crop health, disease detection, water and nutrient management, and post-harvest quality control. This review highlights the applications of drone-based HSI in precision agriculture, where it enables early detection of crop stress, accurate yield prediction, and soil health assessment. In post-harvest management, HSI is utilized to monitor food freshness and ripeness and detect potential contaminants, improving food safety and reducing waste. While the benefits of HSI are significant, challenges such as managing large volumes of data, translating spectral information into actionable insights, and ensuring cost-effective access for smallholder farmers remain barriers to its widespread adoption. Looking forward, future directions include advancements in miniaturized sensors, integration with Internet of Things (IoT) devices and satellite data for comprehensive agricultural monitoring, and expanding HSI applications to precision animal sciences. Collaboration among researchers, policymakers, and industry will be crucial to scaling the impact of HSI on global food systems, ensuring sustainable and equitable access to technology.

Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease

Diabetes mellitus is a chronic metabolic disease that increasingly affects people every year. It is known that with its progression and poor management, metabolic changes can lead to organ dysfunctions, including kidneys. The study aimed to combine Raman spectroscopy and biochemical lipid profiling, complemented by machine learning (ML) techniques to evaluate chemical composition changes in kidneys induced by Type 2 Diabetes mellitus (T2DM). Raman spectroscopy identified significant differences in lipid content and specific molecular vibrations, with the 1777 cm-1 band emerging as a potential spectroscopic marker for diabetic kidney damage. The integration of ML algorithms improved the analysis, providing high accuracy, selectivity, and specificity in detecting these changes. Moreover, lipids metabolic profiling revealed distinct variations in the concentration of 11 phosphatydylocholines and 9 acyl-alkylphosphatidylcholines glycerophospholipids. Importantly, the correlation between Raman data and lipids metabolic profiling differed for control and T2DM groups. This study underscores the combined power of Raman spectroscopy and ML in offering a low-cost, fast, precise, and comprehensive approach to diagnosing and monitoring diabetic nephropathy, paving the way for improved clinical interventions. However, taking into account small number of data related to ethical committee approvals, the study should be verified on a larger number of cases.

An overview of the detection methods to the edible oil oxidation degree: Recent progress, challenges, and perspectives

Oil oxidation, the main quality-deteriorated reaction, would significantly and negatively influence its quality and safety during processing and storage. Evaluating oil oxidation degree is an effective strategy to enable early warning and ensure food safety. Herein, principles, recent progresses, advantages and shortcomings, representative applications, current challenges and promising perspectives, and summary tables of traditional (titration), instrumental (chromatography and spectroscopy), and especially rapid detection methods (chemical colorimetric methods and portable miniaturized devices) for evaluating oil oxidation degree are presented and reviewed. It is believed that rapid detection methods are the most promising practical candidate for detecting oil oxidation. Also, the interaction between advanced data-processing techniques and detection methods, and the systematic integration of whole analytical processes is proposed as next-generation perspectives in the oil oxidation evaluation. We wish to provide the knowledge of oil oxidation degree determination and enlighten novel strategies.

Rapid Authentication of Intact Stingless Bee Honey (SBH) by Portable LED-Based Fluorescence Spectroscopy and Chemometrics

Indonesian stingless bee honey (SBH) of Geniotrigona thoracica is popular and traded at an expensive price. Brown rice syrup (RS) is frequently used as a cheap adulterant for an economically motivated adulteration (EMA) in SBH. In this study, authentic Indonesian Geniotrigona thoracica SBH of Acacia mangium (n = 100), adulterated SBH (n = 120), fake SBH (n = 100), and RS (n = 200) were prepared. In short, 2 mL of each sample was dropped directly into an innovative sample holder without any sample preparation including no dilution. Fluorescence intensity was acquired using a fluorescence spectrometer. This portable instrument is equipped with a 365 nm LED lamp as the fixed excitation source. Principal component analysis (PCA) was calculated for the smoothed spectral data. The results showed that the authentic SBH and non-SBH (adulterated SBH, fake SBH, and RS) samples could be well separated using the smoothed spectral data. The cumulative percentage variance of the first two PCs, 98.4749% and 98.4425%, was obtained for calibration and validation, respectively. The highest prediction accuracy was 99.5% and was obtained using principal component analysis-linear discriminant analysis (PCA-LDA). The best partial least square (PLS) calibration was obtained using the combined interval with R2cal = 0.898 and R2val = 0.874 for calibration and validation, respectively. In the prediction, the developed model could predict the adulteration level in the adulterated honey samples with an acceptable ratio of prediction to deviation (RPD) = 2.282, and range error ratio (RER) = 6.612.

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms

The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers), it is vital to identify geographical origin. Near infrared spectroscopy can be used to detect the specific content of organic moieties in agricultural and food products. The present study implemented the combinatorial method of FT-NIR spectroscopy with chemometrics to identify geographical origin of Khao Dawk Mali 105 rice. Rice samples were collected from 2 different region including the north and northeast of Thailand. NIR spectra data were collected in range of 12,500 - 4,000 cm-1 (800-2,500 nm). Five machine learning algorithms including linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), C-support vector classification (C-SVC), backpropagation neural networks (BPNN), hybrid principal component analysis-neural network (PC-NN) and K-nearest neighbors (KNN) were employed to classify NIR data of rice samples with full wavelength and selected wavelength by Extremely Randomized Trees (Extra trees) algorithm. Based on the findings, geographical origin of rice could be specified quickly, cheaply, and reliably using combination of NIRS and machine learning. All models creating by full wavelength and selected wavelength exhibited accuracy between 65 and 100 % for identifying geographical region of rice. It was proven that NIR spectroscopy may be used for the quick and non-destructive identification of geographical origin of Khao Dawk Mali 105 rice.

The method based on ATR-FTIR spectroscopy combined with feature variable selection for the boletus species and origins identification

Wild boletus mushrooms, which are macrofungi of the phylum Basidiomycetes, are a nutritious and unique natural food that is widely enjoyed. Since boletus are consumed with problems of indistinguishable toxic and non-toxic species and heavy metal enrichment, their species identification and traceability are crucial in ensuring quality and safety of consumption. In this study, the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy technique combined with three feature variable extraction methods, manual selection method, semi-manual selection method, and algorithm method, were used to improve the accuracy and computational speed of the model identification, and the models were established for the identification of boletus species with an accuracy of up to 100% as well as for the identification of boletus origin with an accuracy of 86.36%. It was found that the best methods to improve the accuracy of the models were semi-manual selection, manual selection and algorithmic selection in that order. This study can provide rapid and accurate species identification and origin traceability of wild boletus, and provide theoretical basis for the rational use of feature variable selection methods.

Seabream Quality Monitoring Throughout the Supply Chain Using a Portable Multispectral Imaging Device

Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision-making, reducing food waste, and increasing sustainability. A portable multispectral imaging sensor was used for the rapid prediction of microbiological quality of fish fillets. Seabream fillets, packaged either in aerobic or vacuum conditions, were collected from both aquaculture and retail stores, while images were also acquired both from the skin and the flesh side of the fish fillets. In parallel to image acquisition, the microbial quality was also estimated for each fish fillet. The data were used for the training of predictive artificial neural network (ANN) models for the estimation of total aerobic counts (TACs). Models were built separately for fish parts (i.e., skin, flesh) and packaging conditions and were validated using two approaches (i.e., validation with data partitioning and external validation using samples from retail stores). The performance of the ANN models for the validation set with data partitioning was similar for the data collected from the flesh (RMSE = 0.402-0.547) and the skin side (RMSE = 0.500-0.533) of the fish fillets. Similar performance also was obtained from validation of the models of the different packaging conditions (i.e., aerobic, vacuum). The prediction capability of the models combining both air and vacuum packaged samples (RMSE = 0.531) was slightly lower compared to the models trained and validated per packaging condition, individually (RMSE = 0.510, 0.516 in air and vacuum, respectively). The models tested with unknown samples (i.e., fish fillets from retail stores-external validation) showed poorer performance (RMSE = 1.061-1.414) compared to the models validated with data partitioning (RMSE = 0.402-0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated. Hence, these outcomes could be beneficial not only for the industry and food operators but also for the authorities and consumers.

Spectrochip-based Calibration Curve Modeling (CCM) for Rapid and Accurate Multiple Analytes Quantification in Urinalysis

Most urine test strips are intended to enable the general population to rapidly and easily diagnose potential renal disorders. It is semi-quantitative in nature, and although the procedure is straightforward, certain factors will affect the judgmental outcomes. This study describes rapid and accurate quantification of twelve urine test strip parameters: leukocytes, nitrite, urobilinogen, protein, pH, occult blood, specific gravity, ketone, bilirubin, glucose, microalbumin, and creatinine using a micro-electromechanical system (MEMS)-based spectrophotometer, known as a spectrochip. For each parameter, absorption spectra were measured three times independently at eight different concentration levels of diluted standard solutions, and the average spectral intensities were calculated to establish the calibration curve under the characteristic wavelength (λ c ). Then, regression analysis on the calibration curve was performed with GraphPad Prism software, which revealed that the coefficient of determination (R 2 ) of the modeled calibration curves was greater than 0.95. This result illustrates that the measurements exceed standard levels, confirming the importance of a spectrochip for routine multi-parameter urine analysis. Thus, it is possible to obtain the spectral signal strength for each parameter at its characteristic wavelength in order to compare directly with the calibration curves in the future, even in situations when sample concentration is unknown. Additionally, the use of large testing machines can be reduced in terms of cost, time, and space by adopting a micro urine testing platform based on spectrochip, which also improves operational convenience and effectively enables point-of-care (POC) testing in urinalysis.

Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy

In this work, a new model with broad utility for quantitative spectroscopy development is reported. A primary objective of this work is to create a novel modeling procedure that may allow for higher automation of the model development process. The fundamental concept is simple yet powerful even for complex spectra and is employed with no additional preprocessing. This approach is applicable for several types of spectroscopic data to develop regression models that have similar or greater quality than the current methods. The key modeling steps are a matrix transformation and subsequent feature selection process that are collectively referred to as iterative regression of corrective baselines (IRCB). The transformed matrix (Xtransform) is a linearized form of the original X data set. Features from Xtransform that are predictive of Y can be ranked and selected by ordinary least-squares regression. The best features (rows of Xtransform) are linear depictions of Y that can be utilized to develop regression models with several machine learning models. The IRCB workflow is first detailed by using a case study of Fourier transform infrared (FTIR) spectroscopy for prepared solutions of a three-component mixture. Next, IRCB is applied and compared to benchmark results for the 2006 "Chimiométrie" near-infrared spectroscopy (NIR) soil composition challenge and Raman measurements of a simulated nuclear waste slurry.

Characterization and shelf life of synbiotic drink powder from porang (Amorphophallus muelleri)

Amorphophallus muelleri BI was included in the Araceae family, which is a type of tuber. It is a tuber with high potential due to its abundant bioactive compounds. Amorphophallus muelleri BI flour (AF) contains a high glucomannan and carbon compounds that serve as nutrients for probiotic bacteria. Although Amorphophallus muelleri BI thrives in Indonesia, its utilization rate in the country remains relatively low and haven't been any studies conducted regarding synbiotic powder from AF. The primary objective of this research is to develop a synergistic beverage enriched with varying concentrations of Amorphophallus muelleri BI as a prebiotic and LA as probiotic (synbiotic). The process starts with culture preparation, synbiotic drink process, synbiotic and microencapsulation, includes the examination of solubility, proximate analysis, calorie content, viability, and shelf life. Results showed that the proximate and solubility had no significant effect. Synbiotic drink powder from AF can be produced using spray dry technology. The highest LA growth was observed when augmenting the AF quantity at a 0.4% concentration, which can be seen from the viability parameter with a value of 7.29 log CFU/g. Samples shelf life at -21 and 3 °C with LA viability critical parameter was determined to be 4 days.

Microbiological and chemical hazards in cultured meat and methods for their detection

Cultured meat, which involves growing meat in a laboratory rather than breeding animals, offers potential benefits in terms of sustainability, health, and animal welfare compared to conventional meat production. However, the cultured meat production process involves several stages, each with potential hazards requiring careful monitoring and control. Microbial contamination risks exist in the initial cell collection from source animals and the surrounding environment. During cell proliferation, hazards may include chemical residues from media components such as antibiotics and growth factors, as well as microbial issues from improper bioreactor sterilization. In the differentiation stage where cells become muscle tissue, potential hazards include residues from scaffolding materials, microcarriers, and media components. Final maturation and harvesting stages risk environmental contamination from nonsterile conditions, equipment, or worker handling if proper aseptic conditions are not maintained. This review examines the key microbiological and chemical hazards that must be monitored and controlled during the manufacturing process for cultured meats. It describes some conventional and emerging novel techniques that could be applied for the detection of microbial and chemical hazards in cultured meat. The review also outlines the current evolving regulatory landscape around cultured meat and explains how thorough detection and characterization of microbiological and chemical hazards through advanced analytical techniques can provide crucial data to help develop robust, evidence-based food safety regulations specifically tailored for the cultured meat industry. Implementing new digital food safety methods is recommended for further research on the sensitive and effective detection of microbiological and chemical hazards in cultured meat.

Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging

Sorbitol derivatives and other additives are commonly used in various products, such as packaging or food packaging, to improve their mechanical, physical, and optical properties. To accurately and precisely evaluate the efficacy of adding sorbitol-type nucleating agents to these articles, their quantitative determination is essential. This study systematically investigated the quantification of sorbitol-type nucleating agents in food packaging made from impact copolymers of polypropylene (PP) and polyethylene (PE) using attenuated total reflectance infrared spectroscopy (ATR-FTIR) together with analysis of principal components (PCA) and machine learning algorithms. The absorption spectra revealed characteristic bands corresponding to the C-O-C bond and hydroxyl groups attached to the cyclohexane ring of the molecular structure of sorbitol, providing crucial information for identifying and quantifying sorbitol derivatives. PCA analysis showed that with the selected FTIR spectrum range and only the first two components, 99.5% of the variance could be explained. The resulting score plot showed a clear pattern distinguishing different concentrations of the nucleating agent, affirming the predictability of concentrations based on an impact copolymer. The study then employed machine learning algorithms (NN, SVR) to establish prediction models, evaluating their quality using metrics such as RMSE, R2, and RMSECV. Hyperparameter optimization was performed, and SVR showed superior performance, achieving near-perfect predictions (R2 = 0.9999) with an RMSE of 0.100 for both calibration and prediction. The chosen SVR model features two hidden layers with 15 neurons each and uses the Adam algorithm, balanced precision, and computational efficiency. The innovative ATR-FTIR coupled SVR model presented a novel and rapid approach to accurately quantify sorbitol-type nucleating agents in polymer production processes for polymer research and in the analysis of nucleating agent derivatives. The analytical performance of this method surpassed traditional methods (PCR, NN).

Spectral insights: Navigating the frontiers of biomedical and microbiological exploration with Raman spectroscopy

Raman spectroscopy (RS), a powerful analytical technique, has gained increasing recognition and utility in the fields of biomedical and biological research. Raman spectroscopic analyses find extensive application in the field of medicine and are employed for intricate research endeavors and diagnostic purposes. Consequently, it enjoys broad utilization within the realm of biological research, facilitating the identification of cellular classifications, metabolite profiling within the cellular milieu, and the assessment of pigment constituents within microalgae. This article also explores the multifaceted role of RS in these domains, highlighting its distinct advantages, acknowledging its limitations, and proposing strategies for enhancement.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Food Safety Issues in the Oltrepò Pavese Area: A SERS Sensing Perspective

Handly and easy-to-use optical instrumentation is very important for food safety monitoring, as it provides the possibility to assess law and health compliances at every stage of the food chain. In particular, the Surface-enhanced Raman Scattering (SERS) method appears highly promising because the intrinsic drawback of Raman spectroscopy, i.e., the natural weakness of the effect and, in turn, of the signal, is overcome thanks to the peculiar interaction between laser light and plasmonic excitations at the SERS substrate. This fact paved the way for the widespread use of SERS sensing not only for food safety but also for biomedicine, pharmaceutical process analysis, forensic science, cultural heritage and more. However, the current technological maturity of the SERS technique does not find a counterpart in the recognition of SERS as a routine method in compliance protocols. This is mainly due to the very scattered landscape of SERS substrates designed and tailored specifically for the targeted analyte. In fact, a very large variety of SERS substrates were proposed for molecular sensing in different environments and matrices. This review presents the advantages and perspectives of SERS sensing in food safety. The focus of the survey is limited to specific analytes of interest for producers, consumers and stakeholders in Oltrepò Pavese, a definite regional area that is located within the district of Pavia in the northern part of Italy. Our attention has been addressed to (i) glyphosate in rice fields, (ii) histamine in a world-famous local product (wine), (iii) tetracycline, an antibiotic often detected in waste sludges that can be dangerous, for instance in maize crops and (iv) Sudan dyes-used as adulterants-in the production of saffron and other spices, which represent niche crops for Oltrepò. The review aims to highlight the SERS performance for each analyte, with a discussion of the different methods used to prepare SERS substrates and the different reported limits of detection.

FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil

Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R2cal (calibration correlation coefficient) equal to 0.9679, R2val (validation correlation coefficient) equal to 0.8474, and R2pred (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R2 equal to 0.9670, cross-validation R2 equal to 0.7149, and R2 of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined.

Special Issue "Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches"

The globalization of the food market has created a pressing need for food producers to meet the ever-increasing demands of consumers while ensuring adherence to stringent food safety and quality standards [...].