Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

Geographical origin determination of the PDO hazelnut (cv. Giresun Tombul) by chemometric analysis of FT-NIR and Raman spectra acquired from shell and kernel

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS-DA (partial least squares-discriminant analysis), and SVM-C (support vector machine-classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS-DA models utilizing FT-NIR (Fourier transform near-infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM-C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT-NIR and Raman spectroscopy, providing a nondestructive, rapid, and user-friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. PRACTICAL APPLICATION: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts.

A geographical origin assessment of Italian hazelnuts: Gas chromatography-ion mobility spectrometry coupled with multivariate statistical analysis and data fusion approach

Hazelnut is a commodity that has gained interest in the food science community concerning its authenticity. The quality of the Italian hazelnuts is guaranteed by Protected Designation of Origin and Protected Geographical Indication certificates. However, due to their modest availability and the high price, fraudulent producers/suppliers blend, or even substitute, Italian hazelnuts with others from different countries, having a lower price, and often a lower quality. To contrast or prevent these illegal activities, the present work investigated the application of the Gas Chromatography-Ion mobility spectrometry (GC-IMS) technique on the hazelnut chain (fresh, roasted, and paste of hazelnuts). The raw data obtained were handled and elaborated using two different ways, software for statistical analysis, and a programming language. In both cases, Principal Component Analysis and Partial Least Squares-Discriminant Analysis models were exploited, to study how the Volatile Organic Profiles of Italian, Turkish, Georgian, and Azerbaijani products differ. A prediction set was extrapolated from the training set, for a preliminary models' evaluation, then an external validation set, containing blended samples, was analysed. Both approaches highlighted an interesting class separation and good model parameters (accuracy, precision, sensitivity, specificity, F1-score). Moreover, a data fusion approach with a complementary methodology, sensory analysis, was achieved, to estimate the performance enhancement of the statistical models, considering more discriminant variables and integrating at the same time further information correlated to quality aspects. GC-IMS could be a key player as a rapid, direct, cost-effective strategy to face authenticity issues regarding the hazelnut chain.

Mid-infrared spectroscopy for large-scale phenotyping of bovine colostrum gross composition and immunoglobulin concentration

Immunoglobulin G is the fundamental antibody for acquisition of passive transfer of immunity in ruminant newborns. Colostrum, in fact, must be administered as soon as possible after birth to ensure a successful transfer of IgG from the dam to the calf. Assessment of colostrum Ig concentration and gross composition via gold standards is expensive, time consuming, and hardly implementable for large-scale investigations. Therefore, in the present study we evaluated the predictive ability of mid-infrared spectroscopy (MIRS) as an indirect determination method. A total of 714 colostrum samples collected within 6 h from parturition from Italian Holstein cows, 30% primiparous and 70% pluriparous, were scanned using a benchtop spectrometer after dilution in pure water. The prediction models were developed by correlating spectral information with the reference measurements: IgG concentration (93.54 ± 33.87 g/L), total Ig concentrations (102.82 ± 35.04 g/L), and content of protein (14.71 ± 3.51%), fat (4.61 ± 3.04%), and lactose (2.36 ± 0.51 mg/100 mg). We found a good to excellent performance in prediction of colostrum IgG concentration and traditional composition traits in cross-validation (R2CV ≥ 0.92) and a promising and good predictive ability in external validation with R2V equal to 0.84, 0.89, and 0.74 for IgG, protein, and fat, respectively. In the case of IgG and protein content, for example, the coefficient of determination in external validation was greater than 0.84. The other Ig fractions, A and M, presented insufficient prediction accuracy likely due to their extremely low concentration compared with IgG (4.56 and 5.06 g/L vs. 93.54 g/L). The discriminant ability of MIRS-predicted IgG and protein content was outstanding when trying to classify samples according to the quality level (i.e., low vs. high concentration of IgG). In particular, the cut-off that better discriminate low- from high-quality colostrum was 75.40 g/L in the case of the MIRS-predicted IgG and 13.32% for the MIRS-predicted protein content. Therefore, MIRS is proposed as a rapid and cheap tool for large-scale punctual IgG, protein, and lactose quantification and for the screening of low-quality samples. From a practical perspective, there is the possibility to install colostrum models in the MIRS benchtop machineries already present in laboratories in charge of official milk testing. Colostrum phenotypes collected on an individual basis will be useful to breeders for the definition of specific selection strategies and to farmers for management scopes. Finally, our findings may be relevant for other stakeholders, given the fact that colostrum is an emerging ingredient for the animal and human food and pharmaceutical industry.

Time Domain NMR Approach in the Chemical and Physical Characterization of Hazelnuts (Corylus avellana L.)

'Tonda Gentile Romana' and 'Tonda di Giffoni' (Corylus avellana L.) are two Italian hazelnut cultivars, recognized under the quality labels "Protected Designation of Origin" (PDO) and "Protected Geographical Indication" (PGI), respectively. Hazelnut seeds are characterized by a complex microstructure and the presence of different physical compartments. This peculiarity has been studied and evidenced by Time Domain (TD) Nuclear Magnetic Resonance (NMR) experiments. This technique allowed the assessment of the presence of different diffusion compartments, or domains, by evaluating the distribution of the spin-spin relaxation time (T₂).The aim of this research was to develop a method based on ¹H NMR relaxometry to study the mobility in fresh hazelnut seeds ('Tonda di Giffoni' and 'Tonda Gentile Romana'), in order to determine differences in seed structure and matrix mobility between the two cultivars. TD-NMR measurements were performed from 8 to 55 °C in order to mimic post-harvest processing as well the microscopic textural properties of hazelnut. The Carr-Purcell-Meiboom-Gill (CPMG) experiments showed five components for 'Tonda Gentile Romana' and four components for 'Tonda di Giffoni' relaxation times. The two slowest components of relaxation (T_2,a about 30-40% of the NMR signal, and T_2,b about 50% of the NMR signal) were attributed to the protons of the lipid molecules organized in the organelles (oleosomes), both for the 'Tonda Gentile Romana' and for the 'Tonda di Giffoni' samples. The component of relaxation T_2,c was assigned to cytoplasmic water molecules, and showed a T₂ value dominated by diffusive exchange with a reduced value compared to that of pure water at the same temperature. This can be attributed to the water molecules affected by the relaxation effect of the cell walls. The experiments carried out as a function of temperature showed, for 'Tonda Gentile Romana', an unexpected trend between 30 and 45 °C, indicating a phase transition in its oil component. This study provides information that could be used to strengthen the specifications underlying the definitions of "Protected Designation of Origin" (PDO) and "Protected Geographical Indication" (PGI).

Rapid Sensing: Hand-Held and Portable FTIR Applications for On-Site Food Quality Control from Farm to Fork

Today, one of the world's biggest problems is the assurance of food integrity from farm to fork. Economically motivated food adulteration and food authenticity problems are increasing daily with considerable health and economic effects. Early detection and prevention of food integrity-related problems could be provided by the application of effective on-site food analysis technologies. FTIR spectroscopy coupled with chemometrics can be used for the rapid quality control of a wide variety of food products with fast, high-throughput, accurate and nondestructive analysis advantages. In particular, hand-held and portable FTIR instruments have the potential to surveil food quality and food safety in various critical segments of the food supply chain. In this review, we explore the abilities of hand-held and portable FTIR spectrometers combined with multivariate statistics to conduct a quality evaluation of various food products in terms of food adulteration and authenticity issues. An examination of the literature showed that comparable results were obtained based on detection limits, correlation coefficient (R²) values, standard error values and discrimination power by using both portable/hand-held FTIR spectrometers and benchtop FTIR spectrometers. In conclusion, this review highlights the potential usefulness of portable and hand-held FTIR spectrometers combined with chemometrics for maintaining the food quality through the presentation of various applications that may shed light for on-site food control at any point of the food supply chain.

Rapid lignin quantification for fungal wood pretreatment by ATR-FTIR spectroscopy

Lignin determination in lignocellulose with the conventional two-step acid hydrolysis method is highly laborious and time-consuming. However, its quantification is crucial to monitor fungal pretreatment of wood, as the increase of acid-insoluble lignin (AIL) degradation linearly correlates with the achievable enzymatic saccharification yield. Therefore, in this study, a new attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy method was developed to track fungal delignification in an easy and rapid manner. Partial least square regression (PLSR) with cross-validation (CV) was applied to correlate the ATR-FTIR spectra with the AIL content (19.9 %-27.1 %). After variable selection and normalization, a PLSR model with a high coefficient of determination (R_CV² = 0.87) and a low root mean square (RMSECV = 0.60 %) were obtained despite the heterogeneous nature of the fungal solid-state fermentation. These results show that ATR-FTIR can reliably predict the AIL content in fungus-treated wood while being a high-throughput method. This novel method can facilitate the transition to the wood-based economy.

Assessment of the Authenticity of Whisky Samples Based on the Multi-Elemental and Multivariate Analysis

Two hundred and five samples of whisky, including 170 authentic and 35 fake products, were analyzed in terms of their elemental profiles in order to distinguish them according to the parameter of their authenticity. The study of 31 elements (Ag, Al, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, U, V, Ca, Fe, K, Mg, P, S, Ti and Zn) was performed using the Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Cold Vapor-Atomic Absorption (CVAAS) techniques. Additionally, the pH values of all samples were determined by pH-meter, and their isotopic ratios of ⁸⁸Sr/⁸⁶Sr, ⁸⁴Sr/⁸⁶Sr, ⁸⁷Sr/⁸⁶Sr and ⁶³Cu/⁶⁵Cu were assessed, based on the number of counts by ICP-MS. As a result of conducted research, elements, such as Mn, K, P and S, were identified as markers of whisky adulteration related to the age of alcohol. The concentrations of manganese, potassium and phosphorus were significantly lower in the fake samples (which were not aged, or the aging period was much shorter than legally required), compared to the original samples (in all cases subjected to the aging process). The observed differences were related to the migration of these elements from wooden barrels to the alcohol contained in them. On the other hand, the sulfur concentration in the processed samples was much higher in the counterfeit samples than in the authentic ones. The total sulfur content, such as that of alkyl sulfides, decreases in alcohol with aging in the barrels. Furthermore, counterfeit samples can be of variable origin and composition, so they cannot be characterized as one group with identical or comparable features. Repeatedly, the element of randomness dominates in the production of these kinds of alcohols. However, as indicated in this work, the extensive elemental analysis supported by statistical tools can be helpful, especially in the context of detecting age-related adulteration of whisky. The results presented in this paper are the final part of a comprehensive study on the influence of selected factors on the elemental composition of whisky.

Fourier transform infrared spectroscopy and chemometrics for the discrimination of animal fur types

Rapid and reliable animal fur identification has remained a challenge for customs inspection. The accurate distinction between fur types has a significant meaning in implementing the correct tariff policy. A variety of analytical methods have been applied to work on distinguishing animal fur types, with tools of microscopy, molecular testing, mass spectrometry, Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. In this research, the capability of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with pattern recognition methods was investigated for the discrimination of animal fur in six types. This work was to explore the non-destructive application of ATR-FTIR technique in discriminant analysis of animal fur. All spectra were collected by ATR-FTIR of the wavenumber ranging from 4000 to 650 cm^-1. Data pretreatments included moving average smoothing and multiplicative scatter correction (MSC). Four supervised classification algorithms were chosen to categorize the types of fur: soft independent modeling of class analogy (SIMCA), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM). PLS-DA and LS-SVM were both effective approaches, with a 100% classification accuracy rate. The accuracy of PCA-LDA and SIMCA was 98.33% and 99.44%, respectively. Furthermore, LS-SVM model obtained using Monte-Carlo sampling method also obtained 100% prediction accuracy, while all other methods produced misclassification. LS-SVM corrected the non-linearities for the animal fur FTIR data but also remarkably improved the prediction performance level. The results of this study revealed that the combination of ATR-FTIR and chemometrics has a huge potential for animal fur discrimination.

Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles. In this sense, we propose and test a new covering array feature selection (CAFS) algorithm coupled to the naïve Bayes classifier (NBC) to discriminate among Amazonian cacao nibs from six cacao clones. The CAFS wrapper approach looks for the wavebands that maximize the F₁-score, and then, are more relevant for classification. For this purpose, cacao pods of six varieties were collected, and their grains were extracted and processed (fermented, dried, roasted, and milled) to obtain cacao nibs. Then from each clone NIR spectral profiles in the range of 1100-2500 nm were extracted, and relevant wavebands were selected using the proposed CAFS algorithm. For comparison, two standard feature selection techniques were implemented the multi-cluster feature selection MCFS and the eigenvector centrality feature selection ECFS. Then, based on the different selected variables, three NBCs were built and compared among them through statistical metrics. The results showed that using the wavebands selected by CAFS, the NBC performed an average accuracy of 99.63%; being this superior to the 94.92% and 95.79% for ECFS and MCFS respectively. These results showed that the wavebands selected by the proposed CAFS algorithm allowed obtaining a better fit concerning other feature selection methods reported in the literature.

New Non-Invasive Method for the Authentication of Apple Cultivars

Food authentication is very important to protect consumers, sellers, and producers from fraud. Although several methods have been developed using a wide range of analytical techniques, most of them require sample destruction and do not allow in situ sampling or analysis, nor reliable quantification of hundreds of molecules at the same time. To overcome these limitations, we have developed and validated a new noninvasive analytical workflow for food authentication. The method uses a functionalized strip to adsorb small molecules from the surface of the food product, followed by gas chromatography–mass spectrometry analysis of the desorbed analytes. We validated the method and applied it to the classification of five different apple varieties. Molecular concentrations obtained from the analysis of 44 apples were used to identify markers for apple cultivars or, in combination with machine learning techniques, to perform cultivar classification. The overall reproducibility of the method was very good, showing a good coefficient of variation for both targeted and untargeted analysis. The approach was able to correctly classify all samples. In addition, the method was also used to detect pesticides and the following molecules were found in almost all samples: chlorpyrifos-methyl, deltamethrin, and malathion. The proposed approach not only showed very good analytical performance, but also proved to be suitable for noninvasive food authentication and pesticide residue analysis.

Handheld Devices for Food Authentication and Their Applications: A Review

This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.

Application of spectral features for separating homochromatic foreign matter from mixed congee

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A method that can separate homochromatic FM in mixed congee was proposed.

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Spectral features of FM and mixed congee were extracted to build recognition model.

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The SVM model achieved high identification rates (99.17%) for homochromatic FM.

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The proposed method is better than computer vision in separating homochromatic FM.

Foreign matter (FM) in mixed congee not only reduces the quality of the congee but may also harm consumers. However, the common computer vision methods with poor recognition ability for the homochromatic FM. This study used hyperspectral reflectance images with the pattern recognition model to detect homochromatic FM on the mixed congee surface. First, spectral features corresponding to homochromatic FM and background were extracted from hyperspectral images. Then, based on the optimal spectral preprocessing method, LDA, K-nearest neighbor, backpropagation artificial neural network, and support vector machine (SVM) were used to classify the spectral features. The results revealed that the SVM model input with raw spectra principal components exhibited optimal identification rates of 99.17%. Finally, most of the pixels for homochromatic FM were classified correctly by using the SVM model. To summarized, hyperspectral images combined with pattern recognition are an effective method for recognizing homochromatic FM in mixed congee.

Diagnosis of Lung Cancer by ATR-FTIR Spectroscopy and Chemometrics

Lung cancer is the leading cause of cancer-related death in the world. Early diagnosis has great significance for the survival of patients with lung cancer. In this paper, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to study the serum samples from patients with lung cancer and healthy people. The results of spectral band area comparison showed that the concentrations of protein, lipid and nucleic acids molecules in serum of patients with lung cancer were increased compared with those in healthy people. The original spectra were preprocessed to improve the accuracy of principal component regression (PCR) and partial least squares-discriminant analysis (PLS-DA) models. PLS-DA results for first derivative spectral data in nucleic acids (1250-1000cm-1) band showed 80% sensitivity, 91.89% specificity and 87.10% accuracy with highR c 2 of 0.8949 andR v 2 of 0.8153, low RMSEC of 0.3136 and RMSEV of 0.4180. It is shown that ATR-FTIR spectroscopy combined with chemometrics might be developed as a simple method for clinical screening and diagnosis of lung cancer.

Kohonen Artificial Neural Network and Multivariate Analysis in the Identification of Proteome Changes during Early and Long Aging of Bovine Longissimus dorsi Muscle Using SWATH Mass Spectrometry

To study proteomic changes involved in tenderization of Longissimus dorsi, Charolais heifers and bulls muscles were sampled after early and long aging (12 or 26 days). Sensory evaluation and instrumental tenderness measurement were performed. Proteins were analyzed by gel-free proteomics. By pattern recognition (principal component analysis and Kohonen's self-organizing maps) and classification (partial least squares-discriminant analysis) tools, 58 and 86 dysregulated proteins were detected after 12 and 26 days of aging, respectively. Tenderness was positively correlated mainly with metabolic enzymes (PYGM, PGAM2, TPI1, PGK1, and PFKM) and negatively with keratins. Downregulation in hemoglobin subunits and carbonic anhydrase 3 levels was relevant after 12 days of aging, while mimecan and collagen chains levels were reduced after 26 days of aging. Bioinformatics indicated that aging involves a prevalence of metabolic pathways after late and long periods. These findings provide a deeper understanding of changes involved in aging of beef and indicate a powerful method for future proteomics studies.

A new method for investigating microbiota-produced small molecules in adenomatous polyps

The intestinal microbiota is composed of a large number of different bacteria that produce a variety of metabolites. Colorectal cancer, which typically develops from adenomatous polyps, is highly influenced by microbiota. Since a variety of molecular changes may occur as these polyps transform from benign tumor to malignant carcinoma, the ability to study the microbiota-produced metabolites can lead to new discoveries about the development and progression of this cancer. However, to address the complexity of the microbiota-produced molecules, novel methods are needed. To this aim, in the present work, we developed a high-throughput metabolomics method to capture the metabolic complexity of the microbiota metabolome adherent to adenomatous polyps and adenocarcinoma. For the first time, the method enables the simultaneous quantification of almost 300 metabolites, while preserving the integrity of the original sample. The metabolomics approach was analytically validated and had excellent performances in terms of recovery, linearity, specificity, intra- and inter-day precision, limits of detection, and quantification. Furthermore, the clinical potential of the method was demonstrated in adenoma collected for a colorectal adenoma study.

Scores selection via Fisher's discriminant power in PCA-LDA to improve the classification of food data

This paper proposes an adaptation of the Fisher's discriminability criterion (named here as discriminant power, DP) for choosing principal components (obtained from Principal Component Analysis, PCA), which will be used to construct supervised Linear Discriminant Analysis (LDA) models for solving classification problems of food data. The proposed PCA-DP-LDA algorithm was then applied to (i) simulated data, (ii) classify soybean oils with respect to expiration date, and (iii) identify cachaça adulteration with wood extracts that simulated aging. For comparison, PCA-DP-LDA was evaluated against conventional PCA-LDA (based on explained variance) and Partial Least Squares-Discriminant Analysis (PLS-DA). Among them, PCA-DP-LDA achieved the most parsimonious and interpretable results, with similar or better classification performance. Therefore, the new algorithm can be considered a good alternative to the already well-established discriminant methods, being potentially applied where the discriminability of the principal components may not follow the same behavior of the explained variance.

Detection and Comparison of Bioactive Compounds in Different Extracts of Two Hazelnut Skin Varieties, Tonda Gentile Romana and Tonda Di Giffoni, Using a Metabolomics Approach

Agro-wastes are one of the major sources for nutritional and therapeutic benefits along with other beneficial properties. Dark brown pellicular pericarp (skin or testa), covering the hazelnut seed, is removed before consumption after the roasting of a kernel. Defatted skins of both hazelnut varieties, Tonda Gentile Romana and Tonda di Giffoni, were profiled by a metabolomics-based approach and this was used to discriminate between these two different hazelnut cultivars. In particular, an untargeted metabolomic extract from hazelnut by-products was investigated by UHPLC-Mass spectrometry followed by multivariate statistics analysis, and significant qualitative and quantitative metabolic differences were observed between them. Samples were also assessed for their total phenolic and antioxidant capacity using two different assays. Although no significant differences were found in total phenolic contents and antioxidant capacity, the Flavone, Flavonol, Flavonoid, and Phenylpropanoid Biosynthesis pathway was significantly higher in the Romana rather than in the Giffoni variety, whereas Myricetin and Syringetin compounds were more representative in Giffoni cultivars. These results indicated that hazelnut skin, especially from the Romana variety, could potentially be used as an ingredient in healthy food. Healthy food is a new food category with an expanding demand from future generations.

Advances in cultivar choice, hazelnut orchard management, and nut storage to enhance product quality and safety: an overview

European hazelnut (Corylus avellana L.) is a major species of interest for nutritional use within the Betulaceae family and its nuts are widely used throughout the world in the chocolate, confectionery, and bakery industries. Recently its cultivation has been expanded in traditional producer countries and established in new places in the southern hemisphere, including Chile, South Africa, and Australia. Introducing hazelnut in new environments could reduce its productivity, lead the trees to experience eco-physiological disorders, and expose the crop to high pressure from common and new pests and diseases. Thus, new approaches in cultivar choice guidance, in the sustainable orchard management and even in nut storage and kernel quality evaluation are urgently required to improve the hazelnut production and processing chain. The main objective of this study was to systematize the published information regarding recent findings about the cultural operations that directly influence nut and kernel quality, support varietal choice for new plantations, and list the recent advances in nut storage and in quality and safety evaluation. © 2020 Society of Chemical Industry.

Rare earth elements (REE) in biology and medicine

This survey reports on topics that were presented at the workshop on “Challenges with Rare Earth Elements. The Periodic Table at work for new Science & Technology” hold at the Academia dei Lincei in November 2019. The herein reported materials refer to presentations dealing with studies and applications of rare earth elements (REE) in several areas of Biology and Medicine. All together they show the tremendous impact REE have in relevant fields of living systems and highlight, on one hand, the still existing knowledge gap for an in-depth understanding of their function in natural systems as well as the very important role they already have in providing innovative scientific and technological solutions in a number of bio-medical areas and in fields related to the assessment of the origin of food and on their manufacturing processes. On the basis of the to-date achievements one expects that new initiatives will bring, in a not too far future, to a dramatic increase of our understanding of the REE involvement in living organisms as well as a ramp-up in the exploitation of the peculiar properties of REE for the design of novel applications in diagnostic procedures and in the set-up of powerful medical devices. This scenario calls the governmental authorities for new responsibilities to guarantee a continuous availability of REE to industry and research labs together with providing support to activities devoted to their recovery/recycling.

Untargeted metabolomics with multivariate analysis to discriminate hazelnut (Corylus avellana L.) cultivars and their geographical origin

BACKGROUND

In the present study a metabolomics-based approach was used to discriminate among different hazelnut cultivars and to trace their geographical origins. Ultra-high-pressure liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UHPLC-ESI/QTOF-MS) was used to profile phenolic and sterolic compounds.

RESULTS

Compounds were identified against an in-house database using accurate monoisotopic mass and isotopic patterns. The screening approach was designed to discern 15 hazelnut cultivars and to discriminate among the geographical origins of six cultivars from the four main growing regions (Chile, Georgia, Italy, and Turkey). This approach allowed more than 1000 polyphenols and sterols to be annotated. The metabolomics data were elaborated with both unsupervised (hierarchical clustering) and supervised (orthogonal projections to latent structures discriminant analysis, OPLS-DA) statistics. These multivariate statistical tools allowed hazelnut samples to be discriminated, considering both 'cultivar type' and 'geographical origin'. Flavonoids (anthocyanins, flavanols and flavonols - VIP scores 1.34-1.49), phenolic acids (mainly hydroxycinnamics - VIP scores 1.35-1.55) together with cholesterol, ergosterol, and stigmasterol derivatives (VIP scores 1.34-1.49) were the best markers to discriminate samples according to geographical origin.

CONCLUSIONS

This work illustrates the potential of untargeted profiling of phenolics and sterols based on UHPLC-ESI/QTOF mass spectrometry to discriminate hazelnut and support authenticity and origin. © 2019 Society of Chemical Industry.

Rapid discrimination of Notoginseng powder adulteration of different grades using FT-MIR spectroscopy combined with chemometrics

Panax Notoginseng is a kind of herb material with high medicinal value, which requires adaptive planting environment, and not can be continuously cultivated in the same ground. Those reasons lead to a large number of low-grade Notoginseng appears in the market. The objective of this study is to discriminate adulterant of Notoginseng of different grades by FT-MIR spectroscopy couple with chemometrics. In the experiment, high-grade Notoginseng was adulterated with 14 blend ratios: 0%, 1%, 3%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of low-grade Notoginseng. All samples were scanned in the range of 4000-400 cm^-1 by FT-MIR spectra instrument in absorption mode. Baseline, standard normal variate (SNV), multiplicative scatter correction (MSC), orthogonal signal correction (OSC), first derivative (D1) with 11-points smoothing and second derivative (D2) with 11-points smoothing were used to preprocess the spectral data, in which Baseline combined with SNV and D1 with 11-points performed best. The spectral data in the range of 1485-405 cm^-1 were selected by interval partial least squares (iPLS) for modeling. Then, Support vector machine (SVM) and linear discriminant analysis (LDA) were applied for modeling analysis. The best result was achieved by SVM, as the classification accuracy was 100%, which indicated that FT-MIR spectroscopy combined with chemometrics was an effective approach to identify Notoginseng powder adulteration. It could detect the blend ratio of 5% (w/w) as well as the blend ratio of over 5%.

Qualitative Analysis of Traditional Italian Dishes: FTIR Approach

Italian cuisine and its traditional recipes experience an ever-increasing popularity around the world. The “Integrated Approach” is the key to modern food research and the innovative challenge for analyzing and modeling agro-food systems in their totality. The present study aims at applying and evaluating Fourier Transformed Infrared (FTIR) spectroscopy for the analysis of complex food matrices and food preparations. Nine traditional Italian recipes, including First courses, One-dish meals, Side courses, and Desserts, were selected and experimentally prepared. Prior to their analysis via FTIR spectroscopy, the samples were homogenized and lyophilized. The IR spectroscopic characterization and the assignment of the main bands was carried out. Numerous peaks, which correspond to functional groups and modes of vibration of the individual components, were highlighted. The spectra are affected by both the preparation procedures, the cooking methods, and the cooking time. The qualitative analysis of the major functional groups can serve as a basis for a discrimination of the products and the investigation of fraud. For this purpose, the FTIR spectra were evaluated using Principal Component Analysis (PCA). Our results show how the utilization of vibrational spectroscopy combined with a well-established chemometric data analysis method represents a potentially powerful tool in research linked to the food sector and beyond. This study is a first step towards the development of new indicators of food quality.

Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars

During the quality inspection control of fruit beverages, some types of adulterations can be detected, such as the addition or substitution with less expensive fruits. To determine whether grape nectars were adulterated by substitution with apple or cashew juice or by a mixture of both, a methodology based on attenuated total reflectance Fourier transform mid infrared spectroscopy (ATR-FTIR) and multivariate classification methods was proposed. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) models were developed as multi-class methods (classes unadulterated, adulterated with cashew and adulterated with apple) with the full-spectra. PLS-DA presented better performance parameters than SIMCA in the classification of samples with just one adulterant, while poor results were achieved for samples with blends of two adulterants when using both classification methods. Three variable selection methods were tested in order to improve the effectiveness of the classification models: interval partial least squares (iPLS), variable importance in projection scores (VIP scores) and a genetic algorithm (GA). Variable selection methods improved the performance parameters for the SIMCA and PLS-DA methods when they were used to predict samples with only one adulterant. Only PLS-DA coupled with iPLS was able to classify samples with blends of two adulterants, providing sensitivity values between 100% and 83% at 100% specificity for the three studied classes.