scholarly journals Sequential Data Fusion Techniques for the Authentication of the P.G.I. Senise (“Crusco”) Bell Pepper

2021 ◽  
Vol 11 (4) ◽  
pp. 1709
Author(s):  
Alessandra Biancolillo ◽  
Francesca Di Donato ◽  
Francesco Merola ◽  
Federico Marini ◽  
Angelo Antonio D’Archivio
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Near Infrared ◽  
External Validation ◽  
Classification Problem ◽  
Sweet Pepper ◽  
Bell Pepper ◽  
Spectroscopic Techniques ◽  
Sequential Data ◽  
Linear Discriminant

Bell pepper is the common name of the berry obtained from some varieties of the Capsicum annuum species. This agro-food is appreciated all over the world and represents one of the key ingredients of several traditional dishes. It is used as a fresh product, or dried and ground as a seasoning (e.g., paprika). Specific varieties of sweet pepper present organoleptic peculiarities and they have been awarded by quality marks as a further confirmation of their unicity (e.g., Piment d’Espelette, Pimiento de Herbón, Peperone di Senise). Due to the market value of this aliment, it can be subjected to frauds, such as adulterations and sophistication. The present study lays on these considerations and aims at developing a spectroscopy-based approach for authenticating Senise bell pepper and for detecting its adulteration with common paprika. In order to achieve this goal, 60 pure samples of bell pepper from Senise were analyzed by mid- and near-infrared spectroscopies. Then, in order to mimic the adulteration, 40 mixtures of Senise bell pepper and paprika were prepared and analyzed (by the same spectroscopic techniques). Eventually, two different multi-block classification approaches (sequential and orthogonalized partial least squares linear discriminant analysis and sequential and orthogonalized covariance selection linear discriminant analysis) were used to discriminate between pure and adulterated Senise bell pepper samples. Both proposed procedures achieved extremely successful results in external validation, correctly classifying all the (thirty-five) test samples, indicating that both approaches represent a winning solution for the investigated classification problem.

scholarly journals Discrimination of E uterpe oleracea  Mart. (Açaí) and E uterpe edulis  Mart. (Juçara) Intact Fruit Using Near-Infrared (NIR) Spectroscopy and Linear Discriminant Analysis

2015 ◽  
Vol 39 (6) ◽  
pp. 2856-2865 ◽  
Author(s):  
Yara Gurgel Dall' Acqua ◽  
Luis Carlos Cunha Júnior ◽  
Viviani Nardini ◽  
Valquira Garcia Lopes ◽  
José Dalton da Cruz Pessoa ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Linear Discriminant

scholarly journals Geographical Classification of Italian Saffron (Crocus sativus L.) by Multi-Block Treatments of UV-Vis and IR Spectroscopic Data

Molecules ◽  
2020 ◽  
Vol 25 (10) ◽  
pp. 2332 ◽  
Author(s):  
Alessandra Biancolillo ◽  
Martina Foschi ◽  
Angelo Antonio D’Archivio
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
External Validation ◽  
Central Italy ◽  
Accurate Solution ◽  
Crocus Sativus ◽  
Linear Discriminant ◽  
Ir Spectroscopic

One-hundred and fourteen samples of saffron harvested in four different Italian areas (three in Central Italy and one in the South) were investigated by IR and UV-Vis spectroscopies. Two different multi-block strategies, Sequential and Orthogonalized Partial Least Squares Linear Discriminant Analysis (SO-PLS-LDA) and Sequential and Orthogonalized Covariance Selection Linear Discriminant Analysis (SO-CovSel-LDA), were used to simultaneously handle the two data blocks and classify samples according to their geographical origin. Both multi-block approaches provided very satisfying results. Each model was investigated in order to understand which spectral variables contribute the most to the discrimination of samples, i.e., to the characterization of saffron harvested in the four different areas. The most accurate solution was provided by SO-PLS-LDA, which only misclassified three test samples over 31 (in external validation).

Appropriateness of Linear Discriminant and Multinomial Classification Analysis in Marketing Research

1978 ◽  
Vol 15 (1) ◽  
pp. 103-112 ◽  
Author(s):  
William R. Dillon ◽  
Matthew Goldstein ◽  
Leon G. Schiffman
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Marketing Research ◽  
Classification Problem ◽  
Relative Performance ◽  
New Method ◽  
Classification Methods ◽  
Classification Analysis ◽  
Linear Discriminant ◽  
Usage Behavior

Buyer usage behavior data are used to compare the relative performance of a linear discriminant analysis and several multinomial classification methods. The potential shortcomings of each of the procedures investigated are cited, and a new method for determining the contribution of a variable to discrimination in the context of the multinomial classification problem also is presented.

Classification of freshwater fish species by linear discriminant analysis based on near infrared reflectance spectroscopy

2017 ◽  
Vol 25 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Hao Lv ◽  
Wenjie Xu ◽  
Juan You ◽  
Shanbai Xiong
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Freshwater Fish ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Infrared Reflectance Spectroscopy

Near infrared reflectance spectroscopy was used to discriminate different species of freshwater fish samples. Samples from seven freshwater fish species of the family Cyprinidae (black carp ( Mylopharyngodon piceus), grass carp ( Ctenopharyngodon idellus), silver carp ( Hypophthalmichthys molitrix), bighead carp ( Aristichthys nobilis), common carp ( Cyprinus carpio), crucian ( Carassius auratus), and bream ( Parabramis pekinensis)) were scanned by near infrared reflectance spectroscopy from 1000 nm to 1799 nm. Linear discriminant analysis models were built for the classification of species. We inspected the effect of partial least square, principal component analysis, competitive adaptive reweighted sampling, and fast Fourier transform on linear discriminant analysis. The results showed that the dimension reduction methods worked very well for this example. Linear discriminant analysis models which were combined with principal component analysis and fast Fourier transform could classify accurately all the samples under multiplicative scatter correction pre-processing. According to the loadings in principal component analysis, spectra wavelengths 1000, 1001, 1154, 1208, 1284, 1288, 1497, 1665, and 1770 nm were selected as effective wavelengths in linear discriminant analysis. The discriminant analysis was simplified by using effective wavelengths as independent variables in a linear discriminant analysis model. This study indicated that linear discriminant analysis combined with near infrared reflectance spectroscopy could be an effective strategy for the classification of freshwater fish species.

scholarly journals Classification models of structure - P-glycoprotein activity of drugs

2016 ◽  
Vol 62 (2) ◽  
pp. 173-179
Author(s):  
V.Yu. Grigorev ◽  
S.L. Solodova ◽  
D.E. Polianczyk ◽  
O.A. Raevsky
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
External Validation ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Classification Models ◽  
Qsar Modeling ◽  
Linear Discriminant ◽  
P Glycoprotein ◽  
Dragon Descriptors

Thirty three classification models of substrate specificity of 177 drugs to P-glycoprotein have been created using of the linear discriminant analysis, random forest and support vector machine methods. QSAR modeling was carried out using 2 strategies. The first strategy consisted in search of all possible combinations from 1¸5 descriptors on the basis of 7 most significant molecular descriptors with clear physico-chemical interpretation. In the second case forward selection procedure up to 5 descriptors, starting from the best single descriptor was used. This strategy was applied to a set of 387 DRAGON descriptors. It was found that only one of 33 models has necessary statistical parameters. This model was designed by means of the linear discriminant analysis on the basis of a single descriptor of H-bond (SCad). The model has good statistical characteristics as evidenced by results to both internal cross-validation, and external validation with application of 44 new chemicals. This confirms an important role of hydrogen bond in the processes connected with penetration of chemical compounds through a blood-brain barrier

scholarly journals Near-infrared auto-fluorescence spectroscopy combining with Fisher’s linear discriminant analysis improves intraoperative real-time identification of normal parathyroid in thyroidectomy

BMC Surgery ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Junsong Liu ◽  
Xiaoxia Wang ◽  
Rui Wang ◽  
Chongwen Xu ◽  
Ruimin Zhao ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Fluorescence Spectroscopy ◽  
Linear Discriminant Analysis ◽  
Real Time ◽  
Parathyroid Gland ◽  
Near Infrared ◽  
Parathyroid Glands ◽  
Linear Discriminant ◽  
Auto Fluorescence ◽  
Fisher’S Linear Discriminant

Abstract Background To evaluate the efficacy of a sensitive, real-time tool for identification and protection for parathyroid glands during thyroidectomy. Methods Near-infrared (NIR) auto-fluorescence was measured intraoperatively from 20 patients undergoing thyroidectomy. Spectra were measured from suspicious parathyroid glands and surrounding neck tissues during the operation with a NIR fluorescence system. Fast frozen sections were performed on the suspicious parathyroid glands. Accuracy was evaluated by comparison with histology and NIR identification. Data were attracted for Fisher’s linear discriminant analysis. Results The auto-fluorescence intensity of parathyroid was significantly higher than that of thyroid, fat and lymph node. The peak intensity of auto-fluorescence from parathyroid was 5.55 times of that from thyroid at the corresponding wave number. Of the 20 patients, the parathyroid was accurately detected and identified in 19 patients by NIR system, compared with their histologic results. One suspicious parathyroid did not exhibit typical spectra, and was proved to be fat tissue by histology. The NIR auto-fluorescence method had a 100% sensitivity of parathyroid glands identification and a high accuracy of 95%. The positive predictive value was 95%. The parathyroid gland have specific auto-fluorescence spectrum and can be separated from the other three samples through the Fisher’s linear discriminant analysis. Conclusions NIR auto-fluorescence spectroscopy can accurately identify normal parathyroid gland during thyroidectomy. The Fisher’s linear discriminant analysis demonstrated the specificity of the NIR auto-fluorescence of parathyroid tissue and its efficacy in parathyroid discrimination.

Rapid Discrimination of Apple Varieties via Near-Infrared Reflectance Spectroscopy and Fast Allied Fuzzy C-Means Clustering

2015 ◽  
Vol 11 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Xiaohong Wu ◽  
Bin Wu ◽  
Jun Sun ◽  
Min Li
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Clustering Algorithm ◽  
Near Infrared ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Fuzzy C Means ◽  
Apple Varieties ◽  
Possibilistic Clustering

Abstract Discrimination of apple varieties plays an important role in apple post-harvest commercial processing. A fast allied fuzzy c-means (FAFCM) clustering algorithm was proposed to classify the apple varieties using near-infrared reflectance (NIR) spectroscopy technology and orthogonal linear discriminant analysis (OLDA) which was used as feature extraction and dimensionality reduction method. Our classification method: the high-dimensional NIR data were reduced to three-dimensional data by OLDA at first, and the FAFCM clustering algorithm was implemented to classify the reduced data. Furthermore, the principal component analysis (PCA) and linear discriminant analysis (LDA) combined with k-nearest neighbor classifier (KNNC), fuzzy c-means (FCM) clustering and unsupervised possibilistic clustering algorithm (UPCA), formed the other four classification methods to classify apple samples in comparison with our proposed method. The experimental results showed that FAFCM achieved the best performance of classification.

Near infrared reflectance spectrometry classification of lettuce using linear discriminant analysis

2015 ◽  
Vol 7 (5) ◽  
pp. 1890-1895 ◽  
Author(s):  
Anna Luiza Bizerra Brito ◽  
Dimitri Albuquerque Araújo ◽  
Márcio José Coelho Pontes ◽  
Liliana Fátima Bezerra Lira Pontes
Keyword(s):  
Discriminant Analysis ◽  
Variable Selection ◽  
Linear Discriminant Analysis ◽  
Near Infrared ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Reflectance Spectrometry

This study proposes a methodology for lettuce classification employing near infrared reflectance spectrometry and variable selection.

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