Near infrared spectroscopy is suitable for the classification of hazelnuts according to Protected Designation of Origin

Lipids in a Nutshell: Quick Determination of Lipid Content in Hazelnuts with NIR Spectroscopy

Hazelnuts (Corylus avellana L.) are among the most consumed dry fruits all over the world. Their commercial quality is defined, above all, by origin and dimension, as well as by lipid content. Evaluation of this parameter is currently performed with chemical methods, which are expensive, time consuming, and complex. In the present work, the near-infrared (NIR) spectroscopy, using both a benchtop research spectrometer and a retail handheld instrument, was evaluated in comparison with the traditional chemical approach. The lipid content of hazelnuts from different growing regions of origin (Italy, Chile, Turkey, Georgia, and Azerbaijan) was determined with two NIR instruments: a benchtop FT-NIR spectrometer (Multi Purpose Analyser-MPA, by Bruker), equipped with an integrating sphere and an optic fibre probe, and the pocket-sized, battery-powered SCiO molecular sensor (by Consumer Physics). The Randall/Soxtec method was used as the reference measurement of total lipid content. The collected NIR spectra were inspected through multivariate data analysis. First, a Principal Component Analysis (PCA) model was built to explore the information contained in the spectral datasets. Then, a Partial Least Square (PLS) regression model was developed to predict the percentage of lipid content. PCA showed samples distributions that could be linked to their total crude fat content determined with the Randall/Soxtec method, confirming that a trend related to the lipid content could be detected in the spectral data, based on their chemical profiles. PLS models performed better with the MPA instrument than SCiO, with the highest R² of prediction (R²_PRED = 0.897) achieved by MPA probe, while this parameter for SCiO was much lower (R²_PRED = 0.550). Further analyses are necessary to evaluate if more acquisitions may lead to better performances when using the SCiO portable spectrometer.

Simultaneous sex and species classification of silkworm pupae by NIR spectroscopy combined with chemometric analysis

BACKGROUND

Most studies only focus on the sex discrimination of silkworm pupae. However, species differentiation of silkworm pupae is also needed in sericulture. To classify the sex and species at the same time, the present study adopts near infrared (NIR) spectroscopy combined with multivariate analysis.

RESULTS

First, spectra samples were acquired using an NIR sensor, comprising female and male silkworm pupae from three species. Second, three different variables selection approaches were used, including a successive projections algorithm, competitive adaptive reweighted sampling (CARS) and interval partial least squares (iPLS). Third, identification models were built based on random forest and partial least squares discriminant analysis (PLSDA). The experimental results show that iPLS-PLSDA model (95.24%) gives a high performance when using the one of the three variable selection methods alone. To further increase the performance, the variable selection methods are optimized. The accuracy of the iPLS-CARS-PLSDA model is as high as 98.41%.

CONCLUSION

The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for sex and species identification of silkworm pupae. © 2020 Society of Chemical Industry.

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.

Determination of the Geographical Origin of Walnuts (Juglans regia L.) Using Near-Infrared Spectroscopy and Chemometrics

The prices of walnuts vary according to their geographical origin and, therefore, offer a financial incentive for adulteration. A reliable analysis method is required to quickly detect possible misdeclarations and thus prevent food fraud. In this study, a method to distinguish between seven geographical origins of walnuts using Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics as a fast, versatile, and easy to handle analytical tool was developed. NIR spectra of 212 ground and afterwards freeze-dried walnut samples, harvested in three consecutive years (2017-2019), were collected. We optimized the data pre-processing by applying and evaluating 50,545 different pre-processing combinations, followed by linear discriminant analysis (LDA) which was confirmed by nested cross-validation. The results show that in the scope of our research minimal pre-processing led to the best results: By applying just multiplicative scatter correction (MSC) and median centering, a classification accuracy of 77.00% ± 1.60% was achieved. Consequently, this complex model can be used to answer economically relevant questions e.g., to distinguish between European and Chinese walnuts. Furthermore, the great influence of the applied pre-processing methods, e.g., the selected wavenumber range, on the achieved classification accuracy is shown which underlines the importance of optimization of the pre-processing strategy.

NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef "Cecina"

For Protected Geographical Indication (PGI)-labeled products, such as the dry-cured beef meat "cecina de León", a sensory analysis is compulsory. However, this is a complex and time-consuming process. This study explores the viability of using near infrared spectroscopy (NIRS) together with artificial neural networks (ANN) for predicting sensory attributes. Spectra of 50 samples of cecina were recorded and 451 reflectance data were obtained. A feedforward multilayer perceptron ANN with 451 neurons in the input layer, a number of neurons varying between 1 and 30 in the hidden layer, and a single neuron in the output layer were optimized for each sensory parameter. The regression coefficient R squared (RSQ > 0.8 except for odor intensity) and mean squared error of prediction (MSEP) values obtained when comparing predicted and reference values showed that it is possible to predict accurately 23 out of 24 sensory parameters. Although only 3 sensory parameters showed significant differences between PGI and non-PGI samples, the optimized ANN architecture applied to NIR spectra achieved the correct classification of the 100% of the samples while the residual mean squares method (RMS-X) allowed 100% of non-PGI samples to be distinguished.

Hyperspectral imaging and improved feature variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening

Fusarium head blight (FHB), a fungus disease of small grain cereal crops, results in reduced yields and diminished value of harvested grain due to the presence of deoxynivalenol (DON), a mycotoxin produced by the causal pathogen Fusarium graminearum. DON and other tricothecene mycotoxins pose serious health risks to both humans and livestock, especially swine. Due to these health concerns, barley used for malting, food or feed is routinely assayed for DON levels. Various methods are available for assaying DON levels in grain samples including enzyme-linked immunosorbent assay (ELISA) and gas chromatography-mass spectrometry (GC-MS). ELISA and GC-MS are very accurate; however, assaying grain samples by these techniques are laborious, expensive and destructive. In this study, we explored the feasibility of using hyperspectral imaging (382-1030 nm) to develop a rapid and non-destructive protocol for assaying DON in barley kernels. Samples of 888 and 116 from various genetic lines were selected for calibration and prediction. Full-wavelength locally weighted partial least squares regression (LWPLSR) achieved high accuracy with the coefficient of determination in prediction (R²_P) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. Competitive adaptive reweighted sampling (CARS) was used to choose potential feature wavelengths, and these selected variables were further optimized using the iterative selection of successive projections algorithm (ISSPA). The CARS-ISSPA-LWPLSR model developed using 7 feature variables yielded R²_P of 0.680 and RMSEP of 4.213 in DON content prediction. Based on the 7 wavelengths selected by CARS-ISSPA, partial least square discriminant analysis (PLSDA) discriminated barley kernels having lower DON (less than1.25 mg/kg) levels from those with higher levels (including 1.25-3 mg/kg, 3-5 mg/kg, and 5-10 mg/kg), with Matthews correlation coefficient in cross-validation (M-R_CV) of as high as 0.931. The results demonstrate that hyperspectral imaging have potential for accelerating non-destructive DON assays of barley samples.

Classification of Different Blueberry Cultivars by Analysis of Physical Factors, Chemical and Nutritional Ingredients, and Antioxidant Capacities

Blueberry fruits of different cultivars are featured with different quality indices. In this work, three types of quality factors, including 6 physical parameters, 12 chemical and nutritional components, and 3 antioxidant indices, were measured to compare and classify blueberry fruits from 12 different cultivars in China. Using the autoscaled data of quality factors, unsupervised principal component analysis was performed for exploratory analysis of intercultivar differences and the influences of quality factors. A supervised classification method, partial least squares discriminant analysis (PLSDA), was combined with the global particle swarm optimization algorithm (PSO) and two multiclass strategies, one-versus-rest (OVR) and one-versus-one (OVO), to select discriminative quality factors and develop classification models of the 12 cultivars. As a result, OVO-PLSDA with 8 quality factors could achieve the classification accuracy of 0.915. This study will provide new insights into the quality variations and key factors among different blueberry cultivars.

The Inorganic Component as a Possible Marker for Quality and for Authentication of the Hazelnut's Origin

The inorganic component of hazelnuts was considered as a possible marker for geographical allocation and for the assessment of technological impact on their quality. The analyzed samples were Italian hazelnuts of the cultivar Tonda Gentile Romana and Turkish hazelnuts of the cultivars Tombul, Palaz and Çakildak. The hazelnuts were subjected to different drying procedures and different conservative methods. The concentration of 13 elements, namely Ba, Ca, Cu, Fe, K, Mg, Mn, Na, Ni, P, Sn, Sr and Zn, were quantified by inductively coupled plasma optical emission spectroscopy (ICP-OES). All the samples were previously digested in a microwave oven. Before proceeding with the analysis of the samples, the whole procedure was optimized and tested on a certified reference material. The results show that the inorganic component: (i) can represent a fingerprint, able to identify the geographical origin of hazelnuts, becoming an important quality marker for consumer protection; (ii) is strongly influenced by the treatments undergone by the investigated product during all the processing stages. A pilot study was also carried out on hazelnuts of the cultivar Tonda Gentile Trilobata Piemontese, directly harvested from the plant during early development to maturity and analyzed to monitor the element concentration over time.

Non-Targeted HPLC-UV Fingerprinting as Chemical Descriptors for the Classification and Authentication of Nuts by Multivariate Chemometric Methods

Recently, the authenticity of food products has become a great social concern. Considering the complexity of the food chain and that many players are involved between production and consumption; food adulteration practices are rising as it is easy to conduct fraud without being detected. This is the case for nut fruit processed products, such as almond flours, that can be adulterated with cheaper nuts (hazelnuts or peanuts), giving rise to not only economic fraud but also important effects on human health. Non-targeted HPLC-UV chromatographic fingerprints were evaluated as chemical descriptors to achieve nut sample characterization and classification using multivariate chemometric methods. Nut samples were extracted by sonication and centrifugation, and defatted with hexane; extracting procedure and conditions were optimized to maximize the generation of enough discriminant features. The obtained HPLC-UV chromatographic fingerprints were then analyzed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to carry out the classification of nut samples. The proposed methodology allowed the classification of samples not only according to the type of nut but also based on the nut thermal treatment employed (natural, fried or toasted products).

A review on the application of vibrational spectroscopy to the chemistry of nuts

Nuts are highly appreciated due to their nutritional relevance and flavour, being the source of many desirable and healthy compounds as polyunsaturated fatty acids and antioxidants. Their characterization became the target of many studies in the last years through conventional analytical techniques as chromatographic ones. Due to the limitations associated to these techniques, as time, cost and environmental concerns, spectroscopic techniques have been increasingly pointed as reliable alternatives. Either applied to raw materials quality control or to more complex process, as industrial in-line monitoring, spectroscopic techniques, namely vibrational spectroscopy, are gathering strong acceptance. This paper presents a review on the application of vibrational spectroscopy, infrared and Raman, to nuts characterization. Estimates of several qualitative and quantitative parameters, origin authentication and/or adulteration in almonds, peanuts, pine nuts, hazelnuts, walnuts, Brazil nuts, cashews, chestnuts and pistachios will be covered. Advantages and limitations of these techniques and future trends will also be discussed.

1H NMR Spectroscopy for Determination of the Geographical Origin of Hazelnuts

A total of 262 authentic samples was analyzed by ¹H NMR spectroscopy for the geographical discrimination of hazelnuts ( Corylus avellana L.) covering samples from five countries (Germany, France, Georgia, Italy, and Turkey) and the harvest years 2013-2016. This article describes method development starting with an extraction protocol suitable for separation of polar and nonpolar metabolites in addition to reduction of macromolecular components. Using the polar fraction for data analysis, principle component analysis was applied and used to monitor sample preparation and measurement. Several machine learning algorithms were tested to build a classification model. The best results were obtained by a linear discrimination analysis applying a random subspace algorithm. The division of the samples in a trainings set and a test set yielded a cross validation accuracy of 91% for the training set and an accuracy of 96% for the test set. The identification of key features was carried out by Kruskal-Wallis test and t test. A feature assigned to betaine exhibits a significant level for the classification of all five countries and is considered a possible candidate for the development of targeted approaches. Further, the results were compared to a previously published study based on LC-MS analysis of nonpolar metabolites. In summary, this study shows the robustness and high accuracy of a discrimination model based on NMR analysis of polar metabolites.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

Protected Designation of Origin (PDO), Protected Geographical Indication (PGI) and Traditional Speciality Guaranteed (TSG): A bibiliometric analysis

Despite the importance of the literature on food quality labels in the European Union (PDO, PGI and TSG), our search did not find any review joining the various research topics on this subject. This study aims therefore to consolidate the state of academic research in this field, and so the methodological option was to elaborate a bibliometric analysis resorting to the term co-occurrence technique. Analysis was made of 501 articles on the ISI Web of Science database, covering publications up to 2016. The results of the bibliometric analysis allowed identification of four clusters: "Protected Geographical Indication", "Certification of Olive Oil and Cultivars", "Certification of Cheese and Milk" and "Certification and Chemical Composition". Unlike the other clusters, where the PDO label predominates, the "Protected Geographical Indication" cluster covers the study of PGI products, highlighting analysis of consumer behaviour in relation to this type of product. The focus of studies in the "Certification of Olive Oil and Cultivars" cluster and the "Certification of Cheese and Milk" cluster is the development of authentication methods for certified traditional products. In the "Certification and Chemical Composition" cluster, standing out is analysis of the profiles of fatty acids present in this type of product.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Food Fingerprinting: Metabolomic Approaches for Geographical Origin Discrimination of Hazelnuts (Corylus avellana) by UPLC-QTOF-MS

Ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was used for geographical origin discrimination of hazelnuts (Corylus avellana L.). Four different LC-MS methods for polar and nonpolar metabolites were evaluated with regard to best discrimination abilities. The most suitable method was used for analysis of 196 authentic samples from harvest years 2014 and 2015 (Germany, France, Italy, Turkey, Georgia), selecting and identifying 20 key metabolites with significant differences in abundancy (5 phosphatidylcholines, 3 phosphatidylethanolamines, 4 diacylglycerols, 7 triacylglycerols, and γ-tocopherol). Classification models using soft independent modeling of class analogy (SIMCA), linear discriminant analysis based on principal component analysis (PCA-LDA), support vector machine classification (SVM), and a customized statistical model based on confidence intervals of selected metabolite levels were created, yielding 99.5% training accuracy at its best by combining SVM and SIMCA. Forty nonauthentic hazelnut samples were subsequently used to estimate as realistically as possible the prediction capacity of the models.