scholarly journals Classification of Browning on Intact Table Grape Bunches Using Near-Infrared Spectroscopy Coupled With Partial Least Squares-Discriminant Analysis and Artificial Neural Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Andries J. Daniels ◽  
Carlos Poblete-Echeverría ◽  
Hélène H. Nieuwoudt ◽  
Nicolene Botha ◽  
Umezuruike Linus Opara
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Near Infrared ◽  
Table Grape ◽  
Kappa Score ◽  
Specificity And Sensitivity ◽  
Class 1

Table grape browning is a complex physiological disorder that occurs during cold storage. There is a need to investigate novel and innovative ways to manage the problem that hampers the progressive and sustainable growth of table grape industries. Given the complex nature of the browning phenomenon, techniques such as near-infrared (NIR) spectroscopy can be utilized for the non-destructive classification of different browning phenotypes. In this study, NIR coupled with partial least squares discriminant analysis (PLS-DA) and artificial neural networks (ANN) were used to classify bunches as either clear or as having chocolate browning and friction browning based on the spectra obtained from intact ‘Regal Seedless’ table grape bunches that were cold-stored over different periods. Friction browning appears as circular spots close to the pedicel area that are formed when table grape berries move against each other, and chocolate browning appears as discoloration, which originates mostly from the stylar-end of the berry, although the whole berry may appear brown in severe instances. The evaluation of the models constructed using PLS-DA was done using the classification error rate (CER), specificity, and sensitivity and for the models constructed using ANN, the kappa score was used. The CER for chocolate browning (25%) was better than that of friction browning (46%) for weeks 3 and 4 for both class 0 (absence of browning) and class 1 (presence of browning). Both the specificity and sensitivity of class 0 and class 1 for friction browning were not as good as that of chocolate browning. With ANN, the kappa score was tested to classify table grape bunches as clear or having chocolate browning or friction browning and showed that chocolate browning could be classified with a strong agreement during weeks 3 and 4 and weeks 5 and 6 and that friction browning could be classified with a moderate agreement during weeks 3 and 4. These results open up new possibilities for the development of quality checks of packed table grape bunches before export. This has a significant impact on the table grape industry for it will now be possible to evaluate bunches non-destructively during packaging to determine the possibility of these browning types being present when reaching the export market.

Easy classification of traditional Minas cheeses using artificial neural networks and discriminant analysis

2017 ◽  
Vol 70 (4) ◽  
pp. 492-498 ◽  
Author(s):  
Leandro S Santos ◽  
Roberta M D Cardozo ◽  
Natália Moreiria Nunes ◽  
Andréia B Inácio ◽  
Ana Clarissa dos S Pires ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Artificial Neural

scholarly journals Multivariate and machine learning approaches for honey botanical origin authentication using near infrared spectroscopy

2019 ◽  
Vol 27 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Vittoria Bisutti ◽  
Roberta Merlanti ◽  
Lorenzo Serva ◽  
Lorena Lucatello ◽  
Massimo Mirisola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Canonical Discriminant Analysis ◽  
Learning Approaches ◽  
Botanical Origin ◽  
Infrared Spectral

In this work the feasibility of near infrared spectroscopy was evaluated combined with chemometric approaches, as a tool for the botanical origin prediction of 119 honey samples. Four varieties related to polyfloral, acacia, chestnut, and linden were first characterized by their physical–chemical parameters and then analyzed in triplicate using a near infrared spectrophotometer equipped with an optical path gold reflector. Three different classifiers were built on distinct multivariate and machine learning approaches for honey botanical classification. A partial least squares discriminant analysis was used as a first approach to build a predictive model for honey classification. Spectra pretreatments named autoscale, standard normal variate, detrending, first derivative, and smoothing were applied for the reduction of scattering related to the presence of particle size, like glucose crystals. The values of the descriptive statistics of the partial least squares discriminant analysis model allowed a sufficient floral group prediction for the acacia and polyfloral honeys but not in the cases of chestnut and linden. The second classifier, based on a support vector machine, allowed a better classification of acacia and polyfloral and also achieved the classification of chestnut. The linden samples instead remained unclassified. A further investigation, aimed to improve the botanical discrimination, exploited a feature selection algorithm named Boruta, which assigned a pool of 39 informative averaged near infrared spectral variables on which a canonical discriminant analysis was assessed. The canonical discriminant analysis accounted a better separation of samples according to the botanical origin than the partial least squares discriminant analysis. The approach used has permitted to achieve a complete authentication of the acacia honeys but not a precise segregation of polyfloral ones. The comparison between the variables important in projection and the Boruta pool showed that the informative wavelengths are partially shared especially in the middle and far band of the near infrared spectral range.

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