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Description of the subject. The article deals with the use of near-infrared spectroscopy (NIR) on fresh apples to determine the phenolic compounds and dry matter content in peel and flesh powders. Objectives. The aim is the rapid and non-destructive determination of these nutritional parameters. Method. Two hundred twenty-nine fruits from 20 varieties were analyzed with NIR spectroscopy and reference methods. Results. Great variability of total phenolic compounds (TPC) in peel and flesh powders was observed among varieties. The dry matter (DM) content also differed greatly between peel and flesh. Calibration and validation models showed high coefficients of determination for the TPC content, which were slightly higher for the peel than for the flesh (R² val = 0.91 and 0.84 respectively). For the DM content, high coefficients of determination and ratios of prediction to deviation (RPDs) were also observed (R² val = 0.94, RPD = 4.8 and R² val = 0.94, RPD = 4.9 for the peel and flesh respectively). Conclusions. Calibration and validation models allow quantitative predictions to be made for TPC and DM content. They confirm the potential of NIRS for predicting the polyphenol content and highlight its potential for determining the DM content, a parameter often neglected in research into apple quality.

Description of the subject. Phenolic compounds (PCs) are the most abundant secondary metabolites in plants. This work was part of a study that sought to develop rapid screening FT-Raman methods for identifying and quantifying classes and/or types of PCs in the dry extracts of plant products. Objectives. Fourier transform Raman spectroscopy (FT-Raman) coupled with chemometric tools was used to characterize and discriminate four families of phenolic compounds: two important classes of phenolic acids, hydroxybenzoic and hydroxycinnamic acids, as well as their derivatives and flavonoids. Method. For this study, 25 standards of phenolic compounds were used (47 standards in total, taking account of the different brands). Repeatability and reproducibility studies were conducted to verify the Raman assignments of gallic acid. Raman characterization with the most significant spectral bands of phenolic compounds was done using spectra ranging from 1,800 to 50 cm-1. A Fisher test was applied to pre-processed Raman spectra (SNV) and 20 Raman scattering signals were used to differentiate each class of phenolic compounds. Results. Hydroxybenzoic acids, hydroxycinnamic acids, their derivatives and flavonoids have been characterized by Raman spectroscopy; bands have been identified and differentiated within and between groups. Conclusions. The scattering intensities located around 1,600-1,699, 1,300-1,400 and below 200 cm-1 were responsible for differentiating 100% of phenolic compound families, classes and subclasses.

Introduction. For dairy producers who want to transform their milk, the ability of milk to coagulate is an important parameter. It makes it possible to transform milk into cheese. Therefore, it is necessary to understand the coagulation process and the techniques to measure it in order to achieve the best transformation performance. The objective of this review is to describe the milk coagulation process, the factors influencing it and the methods for measuring the coagulation of milk at lab level. Literature. The processing of milk into cheese involves three steps: coagulation, dewatering and refining. Coagulation is a key step which involves the use of rennet and depends on several parameters (pH, calcium content, temperature, etc.). Some milks never coagulate. To measure the coagulation ability of milk and identify different parameters in milk coagulation properties, the Formagraph, the computerized renneting meter and the Optigraph have been developed (reference methods). Equations have been developed using infrared spectrometry to predict the parameters obtained by the reference methods. Conclusions. The milk coagulation mechanism is known. However, the issue of non-coagulating milk persists and represents a real challenge in terms of yield. The use of infrared is a faster alternative to reference methods that measure the coagulation properties of milk, but still requires an improvement in prediction equations.

This review provides an overview of the main scientific outputs of a network (Action) supported by the European Cooperation in Science and Technology (COST) in the field of animal science, namely the COST Action Feed for Health (FA0802). The main aims of the COST Action Feed for Health (FA0802) were: to develop an integrated and collaborative network of research groups that focuses on the roles of feed and animal nutrition in improving animal wellbeing and also the quality, safety and wholesomeness of human foods of animal origin; to examine the consumer concerns and perceptions as regards livestock production systems. The COST Action Feed for Health has addressed these scientific topics during the last four years. From a practical point of view three main scientific fields of achievement can be identified: feed and animal nutrition; food of animal origin quality and functionality and consumers' perceptions. Finally, the present paper has the scope to provide new ideas and solutions to a range of issues associated with the modern livestock production system.

The near-infrared microscope (NIRM) is an instrument that has demonstrated its great ability to detect contaminants not by analyzing a single spectrum but rather by analyzing hundreds or thousands of spectra from individual particles, while at the same time being a non-destructive and easy-to-use technique. It has been used for the detection of meat-and-bone meal (MBM) in compound feeds within the context of the European bovine spongiform encephalopathy (BSE; commonly known as "mad cow disease") crisis. This study describes, for the first time, an application of NIRM instrument standardization using a measurement cell in an inter-laboratory study conducted within the framework of a qualitative determination of animal proteins in compound feeds, based on spectra obtained with eight instruments. The standardization cell was assessed for its ability to produce good optical matching of the instruments and/or to evaluate instrument performance.

For the past few years, research has focused on the application of Near Infrared Spectroscopy (NIR) techniques to analyze the quality of apple varieties. As all fruit quality and nutritional parameters are affected by many factors, to standardize the sampling methods prior to analysis is very important. In this study, the intra-fruit variability of apple varieties in terms of quality parameters (sugar, total polyphenol and vitamin C content) was examined. The adequacy of the protocols usually applied to analyze apple quality (reference analyses of a representative sample of the apple and NIR measurements collected at four points 45° from each other in the equatorial region of the apple) was then investigated. The reference values for the quality parameters showed important intra-fruit variability for all quality parameters analyzed. The results also showed that there was little difference between the mean value at the four points and the mean value of the entire apple. This suggested that a mean value obtained from a representative sample of an apple is enough to assess the variability within the fruit and to estimate, with precision, the content of the quality parameters. The results of NIR spectroscopy showed that measurement at the four target points is suitable for predicting quality parameters precisely. The study concluded that standardized reference analyses should be done on a representative fruit sample of an apple and spectral measurements made at four points 45° from each other in the equatorial region of the apple.

The European Union Reference Laboratory for the detection of animal proteins in feedingstuffs (EURL-AP) has developed an online micrographs collection supporting its network activities within the European Union for the detection of prohibited animal by-products in feed. So far, the only official method for detecting these by-products is light microscopy, which is highly dependent on the skills of a microscopist because it relies on particle recognition. In order to help the microscopist network to achieve high proficiency levels, it was necessary to create an online reference tool based on micrographs and accessible via an Intranet platform. Members of the National Reference Laboratories for animal proteins in feedingstuffs (NRL-AP) and the International Association for Feedingstuff Analysis – Section Feedingstuff Microscopy (IAG) have access to this micrographs collection. This paper describes how the online collection was created and what conditions had to be taken into account in creating such a tool. It also describes how information are periodically updated and managed within the context of the large amount of information included in each micrograph. The need for a robust back-office system as the foundation for all the research activities in this project is also covered, and the evaluation of the use of the online collection is discussed.

Honey is a complex and challenging product to analyze due mainly to its composition consisting on various botanical sources. The discrimination of the origin of honey is of prime importance in order to reinforce the consumer trust in this typical food product. But this is not an easy task as usually no single chemical or physical parameter is sufficient. The aim of our paper is to investigate whether FT-Raman spectroscopy as spectroscopic fingerprint technique combined with some chemometric tools can be used as a rapid and reliable method for the discrimination of honey according to their source. In addition to that, different chemometric models are constructed in order to discriminate between Corsican honeys and honey coming from other regions in France, Italy, Austria, Germany and Ireland based on their FT-Raman spectra. These regions show a large variation in their plants. The developed models include the use of exploratory techniques as the Fisher criterion for wavenumber selection and supervised methods as Partial Least Squares-Discriminant Analysis (PLS-DA) or Support Vector Machines (SVM). All these models showed a correct classification ratio between 85% and 90% of average showing that Raman spectroscopy combined to chemometric treatments is a promising way for rapid and non-expensive discrimination of honey according to their origin.

The European project FAIR-CT96-5053 concerned the application of the Fourier transform Raman and infrared spectroscopy in food chemistry and quality control. Our research mainly concerned the study of the potential of Raman spectroscopy and the comparison with the results achieved in infrared spectroscopy. The discrimination of virgin olive oil from other edible oils and the detection and quantification of virgin olive oil adulteration have been experimented with this new technique of fast and non-destructive analysis.

The ban of processed animal proteins (PAPs) in feed for farmed animals led to a significant reduction of the number of bovine spongiform encephalopathy cases. Presently, optical microscopy remains the only reference method for the detection of PAPs to be applied for official control as required by Commission Directive 2003/126/EC. The legislation also foresees that other methods may be applied in addition to classical microscopy, if – for instance – they provide more information about the origin of the animal constituents. Therefore, alternative and complementary techniques were developed as such or in combination. The most promising ones seem to be PCR (Polymerase Chain Reaction), near infrared microscopy and imaging, as well as immunology. Within the framework of a PAP ban regardless of its species origin (total feed ban), most of the studies were mainly focused on the ability of the techniques to detect the presence of PAPs at 0.1% (mass percentage of constituents of animal origin in feed) as indicated as limit of detection in the official method protocol. A possible modification of the legislation requires that the techniques are also able to determine their species origin and to quantify them. The present paper gives a state of the art of the different methods.

The use of chemical herbicides induces negative impacts on the environment, animals, and human health. It also leads to the development of herbicide-resistant weeds. In this context, natural and efficacious herbicides are highly sought after. Essential oils are natural compounds with antibacterial, fungicidal, and phytotoxic properties. For this reason, we studied the post-emergence phytotoxic effect of cinnamon essential oil (cinnamon EO) from Cinnamomum cassia under greenhouse conditions, testing it against Trifolium incarnatum ( T. incarnatum ) and Lolium perenne ( L. perenne ). The content of malondialdehyde (MDA), percentage of water loss, electrolyte leakage, and the fluorescence of treated leaves by cinnamon EO were determined in order to understand the physiological and biochemical responses. In addition, transmission electron microscopy (TEM) was used to study the effect of cinnamon EO on cellular organelles in different tissues of T. incarnatum leaves. Results showed that cinnamon EO quickly induced oxidative stress in treated leaves by increasing MDA content, impacting membrane integrity and causing water loss. TEM observations confirmed the cell desiccation by cellular plasmolysis and showed an alteration of the membrane integrity and chloroplast damages. Moreover, Raman analysis confirms the disturbance of the plant metabolism by the disappearance of some scattering bands which correspond to primary metabolites. Through our finding, we confirm that cinnamon essential oil (EO) could be proposed in the future as a potential bioherbicide and a suitable source of natural phytotoxic compounds with a multisite action on weeds.

Sustainable dairy farms are characterised by the self-production of forage for animal feed. These farms are sometimes located near industrial areas, entailing a risk of food chain contamination with hazardous metals and polycyclic aromatic hydrocarbons (PAHs). Accordingly, evaluating the impact of pollution on forage and milk is of great interest. In this study, the effects of industrial factors on sustainable forage from 43 dairy farms and possible correlations between inorganic elements and PAHs were studied. Spearman's correlation and principal component analysis (PCA) were performed for the forage and milk. Most of the inorganic elements in the forage were below the maximum residual limits for cadmium (Cd) and lead (Pb), established in EU 2013/1275 and EU 2019/1869, respectively. However, arsenic (As) and mercury (Hg) levels were above their respective limits in the forage (EU 2019/1869). No milk samples exceeded the maximum residual limits for Pb (EU 488/2014) or Cd (EU 1881/2006) in dairy products. Heavy-weight PAHs (HW-PAHs, four or more aromatic rings) were detected in forage but not in milk. In the forage samples, HW-PAHs were positively correlated with Zn and Cd. In addition, some hazardous metals (chromium (Cr), iron (Fe), As, Hg, and Pb) also were positively correlated with Zn and Cd. Interestingly, no correlations were found between forage pollutants and milk, suggesting that these pollutants have a low transfer rate to milk. The PCA results highlighted the predominant contribution of PAHs to the global variance in forage samples collected at different distances from industrial areas. In milk, the contributions of hazardous metals and PAHs were more balanced than in forages. Finally, when distances to potential pollution sources were included in the PCA of forage samples, a negative correlation was observed between the former and the concentrations of HW-PAHs, Cd, and Zn, suggesting that thermal power plants and steel factory emissions were the main sources of polluting forage in this area., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Ana Soldado reports financial support has been included in acknowledgements., (© 2023 The Authors. Published by Elsevier Ltd.)

Spectroscopic techniques combined with chemometrics are a promising tool for analysis of seized drug powders. In this study, the performance of three spectroscopic techniques [Mid-InfraRed (MIR), Raman and Near-InfraRed (NIR)] was compared. In total, 364 seized powders were analyzed and consisted of 276 cocaine powders (with concentrations ranging from 4 to 99 w%) and 88 powders without cocaine. A classification model (using Support Vector Machines [SVM] discriminant analysis) and a quantification model (using SVM regression) were constructed with each spectral dataset in order to discriminate cocaine powders from other powders and quantify cocaine in powders classified as cocaine positive. The performances of the models were compared with gas chromatography coupled with mass spectrometry (GC-MS) and gas chromatography with flame-ionization detection (GC-FID). Different evaluation criteria were used: number of false negatives (FNs), number of false positives (FPs), accuracy, root mean square error of cross-validation (RMSECV) and determination coefficients (R2). Ten colored powders were excluded from the classification data set due to fluorescence background observed in Raman spectra. For the classification, the best accuracy (99.7%) was obtained with MIR spectra. With Raman and NIR spectra, the accuracy was 99.5% and 98.9%, respectively. For the quantification, the best results were obtained with NIR spectra. The cocaine content was determined with a RMSECV of 3.79% and a R2 of 0.97. The performance of MIR and Raman to predict cocaine concentrations was lower than NIR, with RMSECV of 6.76% and 6.79%, respectively and both with a R2 of 0.90. The three spectroscopic techniques can be applied for both classification and quantification of cocaine, but some differences in performance were detected. The best classification was obtained with MIR spectra. For quantification, however, the RMSECV of MIR and Raman was twice as high in comparison with NIR. Spectroscopic techniques combined with chemometrics can reduce the workload for confirmation analysis (e.g., chromatography based) and therefore save time and resources., (© The Author(s) 2020. Published by Oxford University Press on behalf of Society of Forensic Toxicologists, Inc. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of Argentina over four harvesting seasons. Each sample was fingerprinted by FT-MIR, NIR and FT-Raman spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Furthermore, low- mid- and high-level data fusion were attempted in order to enhance the classification results. Finally, the best-performing solution underwent to SIMCA modelling with the purpose of reproducing a food authentication scenario. All the developed classification models underwent to a "year-by-year" validation strategy, enabling a sound assessment of their long-term robustness and excluding any issue of model overfitting. Excellent classification scores were achieved by all the technologies and nearly perfect classification was provided by FT-MIR. All the data fusion strategies provided satisfying outcomes, with the mid- and high-level approaches outperforming the low-level data fusion. However, no significant advantage over the FT-MIR alone was obtained. SIMCA modelling of FT-MIR data produced highly sensitive and specific models and an overall prediction ability improvement was achieved when more harvesting seasons were used for the model calibration (86.7% sensitivity and 91.1% specificity). The results obtained in the present work suggested the major potential of FT-MIR for fingerprinting-based honey authentication and demonstrated that accuracy levels that may be commercially useful can be reached. On the other hand, the combination of multiple vibrational spectroscopic fingerprints represents a choice that should be carefully evaluated from a cost/benefit standpoint within the industrial context., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

In the dataset presented in this article, sixty sugarcane samples were analyzed by eight visible / near infrared spectrometers including seven micro-spectrometers. There is one file per spectrometer with sample name, wavelength, absorbance data [calculated as log 10 (1/Reflectance)], and another file for reference data, in order to assess the potential of the micro-spectrometers to predict chemical properties of sugarcane samples and to compare their performance with a LabSpec spectrometer. The Partial Least Square Regression (PLS-R) algorithm was used to build calibration models. This open access dataset could also be used to test new chemometric methods, for training, etc., Competing Interests: None., (© 2020 The Author(s).)

Near infrared microscopy (NIRM) has been developed as a rapid technique to predict the chemical composition of foods, reduce analytical costs and time and ease sample preparation. In this study, NIRM has been evaluated as an alternative to classical chemical analysis to determine the nitrogen and carbon content of small samples of tomato (Solanum lycopersicum L.) leaf powder. Near infrared spectra were obtained by NIRM for independent leaf samples collected on 216 plants grown under six different levels of nitrogen. From these, 30 calibration and 30 validation samples covering the spectral range of the whole set were selected and their nitrogen and carbon contents were determined by a reference method. The calibration model obtained for nitrogen content proved to be excellent, with a coefficient of determination in calibration (R(2)c) higher than 0.9 and a ratio of performance to deviation (RPDc) higher than 3. Statistical indicators of prediction using the validation set were also very high (R(2)p values > 0.90). However, the calibration model obtained for carbon content was much less satisfactory (R(2)c < 0.50). NIRM appears as a promising and suitable tool for a rapid, non-destructive and reliable determination of nitrogen content of tiny samples of tomato leaf powder.

In the context of products from certain regions or countries being banned because of an identified or non-identified hazard, proof of geographical origin is essential with regard to feed and food safety issues. Usually, the product labeling of an affected feed lot shows origin, and the paper documentation shows traceability. Incorrect product labeling is common in embargo situations, however, and alternative analytical strategies for controlling feed authenticity are therefore needed. In this study, distillers' dried grains and solubles (DDGS) were chosen as the product on which to base a comparison of analytical strategies aimed at identifying the most appropriate one. Various analytical techniques were investigated for their ability to authenticate DDGS, including spectroscopic and spectrometric techniques combined with multivariate data analysis, as well as proven techniques for authenticating food, such as DNA analysis and stable isotope ratio analysis. An external validation procedure (called the system challenge) was used to analyze sample sets blind and to compare analytical techniques. All the techniques were adapted so as to be applicable to the DDGS matrix. They produced positive results in determining the botanical origin of DDGS (corn vs. wheat), and several of them were able to determine the geographical origin of the DDGS in the sample set. The maintenance and extension of the databanks generated in this study through the analysis of new authentic samples from a single location are essential in order to monitor developments and processing that could affect authentication.

In this work, the maturity index of different samples of olives was objectively assessed by image analysis obtained through machine vision, in which algorithms of color-based segmentation and operators to detect edges were used. This method allows a fast, automatic and objective prediction of olive maturity index. This prediction value was compared to maturity index (MI), generally used by olive oil industry, based on the subjective visual determination of color of fruit skin and flesh. Machine vision was also applied to the automatic estimation of size and weight of olive fruits. The proposed system was tested to obtain a good performance in the classification of the fruit in batches. When applied to several olive samples, the maturity index predicted by machine vision was in close agreement with the maturity index of fruits visually estimated, values that are currently used as standards. The evaluation of weight of fruit also provided good results (R(2) = 0.91). These results obtained by image analysis can be used as a useful method for the classification of olives at the reception in olive mill, allowing a better quality control of the production process.

Near-infrared microscopy (NIRM) has been proved to be a powerful tool for the detection of banned meat and bone meal (MBM) in feed. The identification of MBM traces and its ability to differentiate animal from vegetable feed ingredients is based on the evaluation of near-infrared spectra obtained from individual particles present in the sample. This evaluation is supported by appropriate decision rules for the absorbances at specific wavelengths. Here we show that the method and the corresponding decision rules can be successfully transferred from the laboratory which constructed the decision rules to two independent laboratories that were not involved in the calibration process of the method. The analytical results from blind feed samples containing MBM (positive samples) and feed samples without MBM (negative samples) revealed a very good agreement between the three laboratories, thus demonstrating the transferability of the method.

In this paper we present a polymerase chain reaction (PCR)-based method for detecting meat and bone meal (MBM) in compound feedingstuffs. By choosing adequate DNA targets from an appropriate localisation in the genome, the real-time PCR method developed here proved to be robust to severe heat treatment of the MBM, showing high sensitivity in the detection of MBM. The method developed here permits the specific detection of processed pig and cattle materials treated at 134 degrees C in various feed matrices down to a limit of detection of about 0.1%. This technique has also been successfully applied to well-characterised MBM samples heated to as high as 141 degrees C, as well as to various blind feed samples with very low MBM contents. Finally, the method also passed several official European ring trials.

The objective of the reported study was to assess the abilities of various methods to differentiate the sources of fats used in feedstuff formulations. The main target was the identification of tallow (ruminant fat) and its differentiation from non-ruminant fats. Four different techniques were compared in terms of their suitability for enforcing existing and upcoming legislation on animal by-products: (1) Fourier transform infrared spectroscopy (FT-IR) applied to fat samples, (2) gas chromatography coupled with mass spectrometry (GC-MS) to determine fatty acid profiles, (3) immunoassays focusing on the protein fraction included in the fat, and (4) polymerase chain reaction (PCR) for the detection of bovine-specific DNA. Samples of the different fats and oils as well as mixtures of these fats were probed using these analytical methods. FT-IR and GC-MS differentiated pure fat samples quite well but showed limited ability to identify the animal species or even the animal class the fat(s) belonged to; no single compound or spectral signal that could permit species identification could be found. However, immunoassays and PCR were both able to identify the species or groups of species that the fats originated from, and they were the only techniques able to identify low concentrations of tallow in a mixture of fats prepared by the rendering industry, even when the samples had been sterilised at temperatures >133 degrees C. Fats used in animal nutrition come mainly from the rendering industry, thereby confirming the suitability of PCR and immunoassays for their identification. However, neither of these latter techniques was able to detect "premier jus" tallow, representing the highest quality standard of fat with extremely low protein concentration.

In this paper we present an alternative method for detection of meat and bone meal (MBM) in feedstuffs by near-infrared microscopic (NIRM) analysis of the particles in the sediment fraction (dense fraction (d >1.62) from dichloroethylene) of compound feeds. To apply this method the particles of the sediment fraction are spread on a sample holder and presented to the NIR microscope. By using the pointer of the microscope the infrared beam is focussed on each particle and the NIR spectrum (1112-2500 nm) is collected. This method can be used to detect the presence of MBM at concentrations as low as 0.05% mass fraction. When results from the NIRM method were compared with the classical microscopic method, a coefficient of determination (R2) of 0.87 was obtained. The results of this study demonstrated that this method could be proposed as a complementary tool for the detection of banned MBM in feedstuffs by reinforcement of the monitoring of feeds.