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Hazelnut market prices fluctuate significantly based on cultivar and provenance, making them susceptible to counterfeiting. To develop an accurate authentication method, we compared the performances of three spectroscopic methods: near infrared (NIR), handheld near infrared (hNIR), and medium infrared (MIR), on over 300 samples from various origins, cultivars, and harvest years. Spectroscopic fingerprints were used to develop and externally validate PLS-DA classification models. Both cultivar and origin models showed high accuracy in external validation. The hNIR model effectively distinguished cultivars but struggled with geographic distinctions due to lower sensitivity. NIR and MIR models showed over 93 % accuracy, with NIR slightly outperforming MIR for geographic origin. NIR proved to be a fast and suitable tool for hazelnut authentication. This study is the first to systematically compare spectroscopic tools for authenticating hazelnut cultivar and origin using the same dataset, offering valuable insights for future food authentication applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Among the severe foodborne illnesses, listeriosis resulting from the pathogen Listeria monocytogenes exhibits one of the highest fatality rates. This study investigated the application of near infrared hyperspectral imaging (NIR-HSI) for the classification of three L. monocytogenes serotypes namely serotype 4b, 1/2a and 1/2c. The bacteria were cultured on Brain Heart Infusion agar, and NIR hyperspectral images were captured in the spectral range 900-2500 nm. Different pre-processing methods were applied to the raw spectra and principal component analysis was used for data exploration. Classification was achieved with partial least squares discriminant analysis (PLS-DA). The PLS-DA results revealed classification accuracies exceeding 80 % for all the bacterial serotypes for both training and test set data. Based on validation data, sensitivity values for L. monocytogenes serotype 4b, 1/2a and 1/2c were 0.69, 0.80 and 0.98, respectively when using full wavelength data. The reduced wavelength model had sensitivity values of 0.65, 0.85 and 0.98 for serotype 4b, 1/2a and 1/2c, respectively. The most relevant bands for serotype discrimination were identified to be around 1490 nm and 1580-1690 nm based on both principal component loadings and variable importance in projection scores. The outcomes of this study demonstrate the feasibility of utilizing NIR-HSI for detecting and classifying L. monocytogenes serotypes on growth media., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Paul James Williams reports financial support was provided by National Research Foundation of South Africa. Rumbidzai T Matenda reports financial support and travel were provided by South Africa Department of Science and Innovation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.]., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

A portable Fourier Transform Mid-InfraRed (FT-MIR) spectrometer using Attenuated Total Reflectance (ATR) sampling is used for daily routine screening of seized powders. Earlier, ATR-FT-MIR combined with Support Vector Machines (SVM) algorithms resulted in a significant improvement of the screening method to a reliable and straightforward classification and quantification tool for both cocaine and levamisole. However, can this tool be transferred to new (hand-held) devices, without loss of the extensive data set? The objective of this study was to perform a calibration transfer between a newly purchased bench top (BT) spectrometer and a portable (P) spectrometer with existing calibration models. Both instruments are from the same brand and have identical characteristics and acquisition parameters (FT instrument, resolution of 4 cm -1 and wavenumber range 4000 to 500 cm -1 ). The original SVM classification model (n = 515) and SVM quantification model (n = 378) were considered for the transfer trial. Three calibration transfer strategies were assessed: 1) adjustment of slope and bias; 2) correction of spectra from the new instrument BT to P using Piecewise Direct Standardization (PDS) and 3) building a new mixed instrument model with spectra of both instruments. For each approach, additional cocaine powders were measured (n = 682) and the results were compared with GC-MS and GC-FID. The development of a mixed instrument model was the most successful in terms of performance. The future strategy of a mixed model allows applying the models, developed in the laboratory, to portable instruments that are used on-site, and vice versa. The approach offers opportunities to exchange data within a network of forensic laboratories using other FT-MIR spectrometers., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The melamine scandal indicates that traditional targeted detection methods only detect the specifically listed forms of contamination, which leads to the failure to identify new adulterants in time. In order to deal with continually changing forms of adulterations in food and feed and make up for the inadequacy of targeted detection methods, an untargeted detection method based on local anomaly detection (LAD) using near infrared (NIR) imaging was examined in this study. In the LAD method, with a particular size of window filter and at a 99% level of confidence, a specific value of Global H (GH, modified Mahalanobis distance) can be used as a threshold for anomalous spectra detection and quantitative analysis. The results showed an acceptable performance for the detection of contaminations with the advantage of no need of building a 'clean' library. And, a high coefficient of determination (R 2 LAD  = 0.9984 and R 2 PLS-DA  = 0.9978) for the quantitative analysis of melamine with a limit of detection lower than 0.01% was obtained. This indicates that the new strategy of untargeted detection has the potential to move from passive to active for food and feed safety control., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Fluorescence spectroscopy is a powerful method for protein analysis. Its sensitivity and selectivity allow its use for the detection of blood meal and blood products. This study proposes a novel approach for the detection of hemoglobin in animal feed by synchronous fluorescence spectroscopy (SFS). The objective was to develop a fast and easy method to detect hemoglobin powder and blood meal. Analyses were carried out on standard reference material (hemoglobin and albumin) in order to optimize SFS method conditions for hemoglobin detection. The method was then applied to protein extracts of commercial feed material and compound feed. The results showed that SFS spectra of blood meal and blood products (hemoglobin powder and plasma powder) could be used to characterize hemoglobin. Principal component analysis (PCA) applied to area-normalized SFS spectra of artificially adulterated samples made it possible to define a limit of detection of hemoglobin powder or blood meal of 0.5-1% depending on the feed material. The projection in the PCA graphs of SFS spectra of real commercial compound feeds known to contain or to be free from blood-derived products showed that it was possible to discriminate samples according to the presence of hemoglobin. These results confirmed that SFS is a promising screening method for the detection of hemoglobin in animal feed., (Copyright © 2018 Elsevier B.V. All rights reserved.)

In this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases., (Copyright © 2016 Elsevier B.V. All rights reserved.)

A new near-infrared (NIR) spectroscopic method was developed for the analytical measurement of lipid oxidation in sunflower kernel paste (tahini), which was chosen as an example of a complex oilseed-based food product. The NIR spectra of sunflower tahini were acquired for the extracted fat phase (EFP) and for the intact sunflower tahini (IST) samples during controlled storage. The best peroxide value (PV) calibration models were considered suitable for quality control (ratio of performance of deviation [RPD]>5). The best PV partial least squares (PLS) model result for EFP (RPD 6.36) was obtained when using standard normal variate (SNV) and the Savitzky-Golay first derivative in the 1140-1184nm, 1388-1440nm and 2026-2194nm regions. In the case of IST spectra, the best PV models (RPD 5.23) were obtained when either multiple scattering correction (MSC) or SNV were followed by the Savitzky-Golay second derivative for the 1148-1180nm and 2064-2132nm regions. There were poor correlations between the NIR-predicted values and the reference data of the p-anisidine value (pAV) for both EFP and IST. Overall, the results obtained showed that NIR spectroscopy is an appropriate analytical tool for monitoring sunflower paste PV in situ. Due to the nonexistence of the extraction step, it demonstrates a unique and substantial advantage over presently known methods. Based on these results it is strongly recommended that, when using NIR PLS models to assess lipid oxidation in situ in similar oilseed-based food products (e.g., sesame tahini, hazelnut and cocoa liquor used for chocolate production, peanut butter, hazelnut, almond, pistachio spreads), suitable calibration sets containing samples of different particle sizes and stored at different temperatures be selected., (Copyright © 2016 Elsevier B.V. All rights reserved.)

The scope of this article is to propose an innovative method based on Near Infrared Hyperspectral Chemical Imaging (NIR-HCI) to rapidly and non-destructively evaluate the relative degree of collagen preservation in bones recovered from archaeological contexts. This preliminary study has allowed the evaluation of the potential of the method using bone samples from the Early Upper Palaeolithic, Mesolithic and Neolithic periods at the site of Trou Al'Wesse in Belgium. NIR-HCI, combined with chemometric tools, has identified specific spectral bands characteristic of collagen. A chemometric model has been built using Partial Least Squares Discriminant Analysis (PLS-DA) to identify bones with and without collagen. This enables the evaluation of the degree of collagen preservation and homogeneity in bones within and between different strata, which has direct implications for archaeological applications (e.g., taphonomic analyses, assemblage integrity) and sample selection for subsequent analyses requiring collagen. Two archaeological applications are presented: comparison between sub-layers in an Early Upper Palaeolithic unit, and evaluation of the range of variability in collagen preservation within a single Holocene stratum., (Copyright © 2014 Elsevier B.V. All rights reserved.)

The objective of this study was to discriminate by a NIR line scan hyperspectral imaging, taxonomic plant families comprised of different grassland species. Plants were collected from semi-natural meadows of the National Apuseni Park, Apuseni Mountains, Gârda area (Romania) according to botanical families. Chemometric tools such as PLS-DA were used to discriminate distinct grassland species, and assign the different species to botanical families. Species within the Poacea family and other Botanical families could be distinguished (R(2)=0.91 and 0.90, respectively) with greater accuracy than those species in the Fabacea family (R(2)=0.60). A correct classification rate of 99% was obtained in the assignment of the various species to the proper family. Moreover a complete study based on wavelength selection has been performed in order to identify the chemical compound related to each botanical family and therefore to the possible toxicity of the plant. This work could be considered as a first step for the development of a complete procedure for the detection and quantification of possible toxic species in semi-natural meadows used by grazing animals., (© 2013 Elsevier B.V. All rights reserved.)

External quality is an important factor in the extraction of olive oil and the marketing of olive fruits. The appearance and presence of external damage are factors that influence the quality of the oil extracted and the perception of consumers, determining the level of acceptance prior to purchase in the case of table olives. The aim of this paper is to report on artificial vision techniques developed for the online estimation of olive quality and to assess the effectiveness of these techniques in evaluating quality based on detecting external defects. This method of classifying olives according to the presence of defects is based on an infrared (IR) vision system. Images of defects were acquired using a digital monochrome camera with band-pass filters on near-infrared (NIR). The original images were processed using segmentation algorithms, edge detection and pixel value intensity to classify the whole fruit. The detection of the defect involved a pixel classification procedure based on nonparametric models of the healthy and defective areas of olives. Classification tests were performed on olives to assess the effectiveness of the proposed method. This research showed that the IR vision system is a useful technology for the automatic assessment of olives that has the potential for use in offline inspection and for online sorting for defects and the presence of surface damage, easily distinguishing those that do not meet minimum quality requirements., (Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.)

Chemometrical models for determination of milk fatty acids (FA) are typically developed using spectral data from a single spectroscopy technique, e.g., mid-infrared spectroscopy in milk control. Such models perform poorly in determining minor components and are highly dependent on the spectral data source and on the type of matrix. In milk fat, the unsuccessful determination of minor (fatty acids lower than 1.0 g/100g in total fat) FA is often the result of: (1) the molecular structure similarity between the minor and the major FA within the milk fat matrix (thus the chemical signature specific to individual fatty acids has restricted specificity), and (2) the low signal intensity (detection limit) for specific vibrational modes. To overcome these limitations, data from different types of spectroscopy techniques, which brings additional chemical information in relation to the variation of the FA, could be included in the regression models to improve quantification. Here, Fourier transform (FT) Raman spectra were concatenated with attenuated total reflectance FT infrared (ATR/FTIR) spectra. The new combinatorial models showed up to 25% decrease in the root mean squared error of cross-validation (RMSECV) values, accompanied with a higher Rcv(2) for most individual FA or sums of FA groups, as compared to regression models based on Raman only or ATR/FTIR only spectra. In addition, improved models included less PLS components indicating an increased robustness. Interpretation of the most contributing regression coefficients indicated the value of newly combined spectral regions as carriers of specific chemical information. Although requiring additional spectroscopy instrumentation and prolonged acquisition time, this new combinatorial approach can be automated and is sufficient for semi-routine determination of the milk FA profile., (Copyright © 2013 Elsevier B.V. All rights reserved.)

In the real marketplace, providing high-quality olive oil is important from the perspective of both consumers and producers. Quality control should meet all requirements in the production process, from farm to packaging. The quality of olive oil can be affected by several factors, including agricultural techniques, seasonal conditions, farming systems, maturity, method and duration of storage, and process technology. The quality of oil produced also depends largely on the quality of the olives. In an enterprise aimed at producing high-quality oils, olives with defects ('ground'; i.e., fallen to the ground) should be separated from healthy fruit ('sound'; i.e., collected directly from the tree), because a very small portion of low-quality fruit can ruin the whole batch. The fruit falls partly because of its maturation process, but also because of pest and disease attack or weather conditions (strong wind). Fruit that has fallen to the ground can suffer a rapid deterioration in quality. Currently, the separation of fruits is based mainly on visual inspection or information provided by the farmer. These are not very reliable procedures. Methods using analytical parameters to characterize the oil, such as acidity and peroxide value, can be applied, but they require a lot of time and materials. Alternative techniques are therefore needed for the rapid and inexpensive discrimination of olives as part of a quality control strategy. The work described here aims to determine the potential of low-resolution Raman spectroscopy for the discrimination of olives before the oil processing stage in order to detect whether they have been collected directly from the tree (i.e., healthy fruit) or not. Low-resolution Raman spectroscopy was applied together with multivariate procedures to achieve this aim. PCA was used to find natural clusters in the data. Supervised classification methods were then applied: Soft Independent Modeling of Class Analogy (SIMCA), PLS Discriminate Analysis (PLS-DA) and K-nearest neighbors (KNN). The best results were obtained using the KNN method, with prediction abilities of 100% for 'sound' and 97% for 'ground' in an independent validation set. These results demonstrated the potential of a portable Raman instrument for detecting good quality olives before the oil processing stage, by developing models that could be applied before this stage, thus contributing to an overall improvement in quality control., (Copyright © 2012 Elsevier B.V. All rights reserved.)

In the present study, different multivariate regression techniques have been applied to two large near-infrared data sets of feed and feed ingredients in order to fulfil the regulations and laws that exist about the chemical composition of these products. The aim of this paper was to compare the performances of different linear and nonlinear multivariate calibration techniques: PLS, ANN and LS-SVM. The results obtained show that ANN and LS-SVM are very powerful methods for non-linearity but LS-SVM can also perform quite well in the case of linear models. Using LS-SVM an improvement of the RMS for independent test sets of 10% is obtained in average compared to ANN and of 24% compared to PLS., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Variable selection has been discussed in many papers and it became an important topic in areas as chemometrics and science in general. Here a backward iterative step-by-step wrapper method is proposed using PLS. The root-mean-square error of prediction (RMSEP) for an independent test set is used as selection criterion to quantify the gain obtained using the selected range of variables. The method has been applied to different data sets and the results obtained revealed that one can improve or at least keep constant the prediction performances of the PLS models compared to the full-spectrum models. Moreover with the advantage that the number of variables is reduced driving to an easier interpretation of the relationship between model and sample composition and/or properties. The aim is not to compare to other variable selection methods but to show that a simple one can improve or at least keep constant the prediction performances of the PLS models by using only a limited number of variables.