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 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 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.

Evaluation of the bony repair in rat cranial defect using near infrared reflectance spectroscopy and discriminant analysis

2017 ◽  
Vol 33 (4) ◽  
pp. 1160-1168 ◽  
Author(s):  
Leomir A. S. de Lima ◽  
Kássio M. G. Lima ◽  
Lana S. S. de Oliveira ◽  
Aurigena A. Araújo ◽  
Raimundo Fernandes de Araújo Junior
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Cranial Defect

Authentication of Whole and Ground Coffee Beans by near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 2 (2) ◽  
pp. 85-92 ◽  
Author(s):  
Gerard Downey ◽  
Jerôme Boussion ◽  
Dominique Beauchêne
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Correct Identification ◽  
Correct Classification Rate ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Identification Rate ◽  
Coffee Beans ◽  
Correct Identification Rate ◽  
Nir Reflectance Spectroscopy

The potential of NIR reflectance spectroscopy for discriminating between pure Arabica and pure Robusta coffees and blends of these two was investigated. Studies were performed on whole and ground beans using a factorial discriminant procedure. For whole beans, in the absence of blended samples, a correct classification rate of 96.2% was achieved. Inclusion of blended samples reduced this figure to between 82.9 and 87.6%. In the case of ground samples, including blends, a correct identification rate of 83.02% was achieved. The molecular basis for discrimination is discussed.

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.

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