scholarly journals Identification and Quantification of Turmeric Adulteration in Egg-Pasta by Near Infrared Spectroscopy and Chemometrics

2020 ◽  
Vol 10 (8) ◽  
pp. 2647 ◽  
Author(s):  
Alessandra Biancolillo ◽  
Angela Santoro ◽  
Patrizia Firmani ◽  
Federico Marini
Keyword(s):  
Analytical Approach ◽  
Near Infrared ◽  
External Validation ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Yellow Color ◽  
Egg Pasta

“Egg pasta” is a kind of pasta prepared by adding eggs in the dough; the color of this product is often associated to its quality, as it is proportional to the quantity of egg present in the dough. A possible adulteration on this product is represented by the addition of turmeric (not reported in the label) in the dough. The inclusion of this ingredient (which is minimal, given the strong coloring power of this spice) fraudulently accentuates the yellow color of the product, making it more attractive to the consumer. Given this scenario, the aim of the present work is to develop an analytical approach suitable at detecting the presence of turmeric as an adulterant in egg pasta. One hundred samples of traditional and adulterated egg pasta were analyzed by NIR spectroscopy and PLS-DA (Partial Least Squares Discriminant Analysis) in order to discriminate adulterated and compliant pasta. The classification model provided a total correct classification rate of 97.5% in external validation (40 samples). Eventually, the adulterant was quantified by PLS. This strategy provided satisfying results, achieving a RMSEP (Root Mean Square Error in Prediction) of 0.112 (%-w/w) in external validation.

Two-dimensional mid and near infrared correlation spectroscopy for bacterial identification

2020 ◽  
pp. 096703352097451
Author(s):  
Pavel Krepelka ◽  
Araceli Bolívar ◽  
Fernando Pérez-Rodríguez
Keyword(s):  
Biological Samples ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Correlation Spectroscopy ◽  
Strain Identification ◽  
Correct Classification ◽  
Bacterial Strains ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Concentration Changes

In recent years, near infrared (NIR) spectroscopy has gained interest as a tool for bacteria strain identification. Although some promising results suggest good applicability of the technique, a better interpretation of the NIR bacterial spectra is still needed. In order to analyze the NIR spectrum of biological samples, a correlation analysis between the NIR and the mid-infrared (mid-IR) spectra was performed. In total, 28 spectra of 8 bacterial strains were acquired and correlated in the NIR and the mid-IR spectral ranges. Some molecular bands (Amide I, P = O stretching, C-H stretching/deformation of polysaccharides) were well correlated, and the effect of concentration changes in these molecules were investigated. Moreover, a model for the NIR spectra classification was created with an overall 85% correct classification rate. Subsequently, only NIR wavelengths with high correlation to important mid-IR peaks were selected. This led to an increase in the correct classification rate to 94%. By correlation between well-established mid-IR peaks and NIR spectra, some relationships in the NIR spectra of biological samples were revealed, which was a step towards better understanding and interpretation of the NIR spectra of biological samples.

scholarly journals Near infrared spectroscopy in the supply chain monitoring of Annurca apple

2019 ◽  
Vol 27 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Marina Buccheri ◽  
Maurizio Grassi ◽  
Fabio Lovati ◽  
Milena Petriccione ◽  
Pietro Rega ◽  
...  
Keyword(s):  
Supply Chain ◽  
Infrared Spectroscopy ◽  
Shelf Life ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Correct Classification ◽  
Geographical Indication ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Campania Region

Annurca is the most cultivated apple variety in the Campania region (Italy). It is an Italian protected geographical indication product and its management must follow a strict product specification which requires a typical postharvest treatment: the fruit must be subjected to a reddening process in air (‘melaio’) that improves the red colour and the flavour of the fruit but is very expensive and time consuming. For this reason there is sometimes a tendency to skip the ‘melaio’ process, but in this case the fruit cannot be labelled as ‘Melannurca Campana PGI’. The purpose of this work was to discriminate ‘melaio’ treated fruit from untreated fruit using near infrared spectroscopy. A further objective of the work was the non-destructive evaluation of the apple storage conditions which can affect the product quality. Fruit of Annurca ‘Rossa del Sud’ subjected or not subjected to the reddening treatment in ‘melaio’ were stored at 0.5℃ in air (Air) or in controlled atmosphere (1%O2, 0.7% CO2) for eight-month duration. Following storage, fruit were analysed for standard maturity indices (flesh firmness, soluble solids, acidity) and the near infrared spectrum of each fruit was collected. The spectral data, subjected to various pre-treatments, were used to calculate a calibration model by applying partial least squares-discriminant analysis. The best model allowed discrimination of fruit immediately after storage under different conditions, but with 0 days of shelf life, to be classified with a 93.3% correct classification rate for the prediction set. However, after seven days of shelf life at 20℃, post-storage, correct classification rate dropped to 70%, but it was always possible to discriminate the two treatments (96.6% correct classification rate). The results of this preliminary work suggest a possible use of the portable near infrared instrument in the monitoring of the Annurca (protected geographical indication) supply chain.

scholarly journals Authentication of Sorrento Walnuts by NIR Spectroscopy Coupled with Different Chemometric Classification Strategies

2020 ◽  
Vol 10 (11) ◽  
pp. 4003 ◽  
Author(s):  
Luigi Amendola ◽  
Patrizia Firmani ◽  
Remo Bucci ◽  
Federico Marini ◽  
Alessandra Biancolillo
Keyword(s):  
Near Infrared ◽  
Unsaturated Fatty Acids ◽  
External Validation ◽  
Value Added ◽  
Nir Spectroscopy ◽  
Selling Price ◽  
Classification Rate ◽  
South Italy ◽  
Organoleptic Characteristics ◽  
Quality Product

Walnuts have been widely investigated because of their chemical composition, which is particularly rich in unsaturated fatty acids, responsible for different benefits in the human body. Some of these fruits, depending on the harvesting area, are considered a high value-added food, thus resulting in a higher selling price. In Italy, walnuts are harvested throughout the national territory, but the fruits produced in the Sorrento area (South Italy) are commercially valuable for their peculiar organoleptic characteristics. The aim of the present study is to develop a non-destructive and shelf-life compatible method, capable of discriminating common walnuts from those harvested in Sorrento (a town in Southern Italy), considered a high quality product. Two-hundred-and-twenty-seven walnuts (105 from Sorrento and 132 grown in other areas) were analyzed by near-infrared spectroscopy (both whole or shelled), and classified by Partial Least Squares-Discriminant Analysis (PLS-DA). Eventually, two multi-block approaches have been exploited in order to combine the spectral information collected on the shell and on the kernel. One of these latter strategies provided the best results (98.3% of correct classification rate in external validation, corresponding to 1 misclassified object over 60). The present study suggests the proposed strategy is a suitable solution for the discrimination of Sorrento walnuts.

Modeling approach for releasing a frankfurter production batch

2019 ◽  
Vol 121 (8) ◽  
pp. 1813-1824
Author(s):  
Ravipim Chaveesuk ◽  
Natthamon Konjanattham
Keyword(s):  
Neural Network ◽  
Physical Properties ◽  
Classification Model ◽  
Correct Classification ◽  
Classification Models ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Content Type ◽  
Release Probability ◽  
Batch Release

Purpose The purpose of this paper is to model the relationship between 11 frankfurter physical properties and their sensory scores to classify a release of frankfurter production batches to the market. Design/methodology/approach Data from 209 frankfurter batches were collected. Market batch release classifications were based on 11 physical properties via predictive and direct classification models. The predictive models under study included a regression, backpropagation neural network (BPN) and radial basis function neural network (RBFN) whereas the direct classification models were logistic regression, BPN and RBFN. Model performance was evaluated via correct classification rate. Findings The 11-7-4 RBFN predictive model proved superior with a 90 percent correct classification rate and 0 percent producer risk while the 11-5-1 RBFN, as a classification model, outperformed with the same level of accuracy, 90 and 0 percent, respectively. Producers prefer the less time-consuming direct classifiers for evaluation. Furthermore, the 11-5-1 RBFN direct classifier revealed that color measurement greatly influenced frankfurter batch release. Increases in redness, yellowness and brownness increased batch release probability. Originality/value This research attempts to establish a novel production batch release model for sausage manufacturing. Key factors can then be optimized for improving batch release probability for implementation throughout the sausage industry.

Commercial Wheatflour Authentication by Discriminant Analysis of near Infrared Reflectance Spectra

1993 ◽  
Vol 1 (4) ◽  
pp. 187-197 ◽  
Author(s):  
A. Sirieix ◽  
G. Downey
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Correct Classification ◽  
Infrared Reflectance ◽  
Correct Classification Rate ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Discriminant Model ◽  
Flour Milling

This paper reports an application of qualitative analysis in the flour milling industry based on near infrared spectroscopy and a factorial discriminant procedure. Samples of different commercial flour types were collected from a number of mills and a discriminant model developed; evaluation of this model on a different set of 99 samples produced a correct classification rate of 97%.

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

scholarly journals Fatty Acid Composition of Cosmetic Argan Oil: Provenience and Authenticity Criteria

Molecules ◽  
2020 ◽  
Vol 25 (18) ◽  
pp. 4080
Author(s):  
Milena Bučar Miklavčič ◽  
Fouad Taous ◽  
Vasilij Valenčič ◽  
Tibari Elghali ◽  
Maja Podgornik ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Discriminant Analysis ◽  
Trans Fatty Acids ◽  
Correct Classification ◽  
Fatty Acid Profiles ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Argan Oil ◽  
Geographical Regions

In this work, fatty-acid profiles, including trans fatty acids, in combination with chemometric tools, were applied as a determinant of purity (i.e., adulteration) and provenance (i.e., geographical origin) of cosmetic grade argan oil collected from different regions of Morocco in 2017. The fatty acid profiles obtained by gas chromatography (GC) showed that oleic acid (C18:1) is the most abundant fatty acid, followed by linoleic acid (C18:2) and palmitic acid (C16:0). The content of trans-oleic and trans-linoleic isomers was between 0.02% and 0.03%, while trans-linolenic isomers were between 0.06% and 0.09%. Discriminant analysis (DA) and orthogonal projection to latent structure—discriminant analysis (OPLS-DA) were performed to discriminate between argan oils from Essaouira, Taroudant, Tiznit, Chtouka-Aït Baha and Sidi Ifni. The correct classification rate was highest for argan oil from the Chtouka-Aït Baha province (90.0%) and the lowest for oils from the Sidi Ifni province (14.3%), with an overall correct classification rate of 51.6%. Pairwise comparison using OPLS-DA could predictably differentiate (≥0.92) between the geographical regions with the levels of stearic (C18:0) and arachidic (C20:0) fatty acids accounting for most of the variance. This study shows the feasibility of implementing authenticity criteria for argan oils by including limit values for trans-fatty acids and the ability to discern provenance using fatty acid profiling.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA) for identification and quantification of olive leaf spot (OLS) disease

2014 ◽  
Vol 2 (1) ◽  
pp. 1-9
Author(s):  
Nawaf Abu-Khalaf ◽  
Mazen Salman
Keyword(s):  
Leaf Spot ◽  
Near Infrared ◽  
Latent Infection ◽  
Linear Method ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Olive Leaf ◽  
Classification Rate ◽  
Infection Period ◽  
Linear Svm

Early detection of plant disease requires usually elaborating methods techniques and especially when symptoms are not visible. Olive Leaf Spot (OLS) infecting upper surface of olive leaves has a long latent infection period. In this work, VIS/NIR spectroscopy was used to determine the latent infection and severity of the pathogens. Two different classification methods were used, Partial Least Squared-Discrimination Analysis (PLS-DA) (linear method) and Support Vector Machine (SVM) (non-linear). SVM-classification was able to classify severity levels 0, 1, 2, 3, 4, and 5 with classification rates of 94, 90, 73, 79, 83 and 100%, respectively The overall classification rate was about 86%. PLS-DA was able to classify two different severity groups (first group with severity 0, 1, 2, 3, and second group with severity 4, 5), with a classification rate greater than 95%. The results promote further researches, and the possibility of evaluation OLS in-situ using portable VIS/NIR devices.

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