<i>Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis</i>

2019 ◽  
Author(s):  
Guangjun N/A Qiu ◽  
Enli N/A Lü ◽  
Ning N/A Wang ◽  
Huazhong N/A Lu
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Sweet Corn ◽  
Corn Seed ◽  
Cultivar Classification

scholarly journals Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis

2019 ◽  
Vol 9 (8) ◽  
pp. 1530 ◽  
Author(s):  
Guangjun Qiu ◽  
Enli Lü ◽  
Ning Wang ◽  
Huazhong Lu ◽  
Feiren Wang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Seed Quality ◽  
Sweet Corn ◽  
Full Range ◽  
Classification Model ◽  
Support Vector ◽  
Corn Seed ◽  
Discriminant Analyses

Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.

scholarly journals Discriminant analysis of Olomouc curd cheese by Fourier transform near infrared spectroscopy

2014 ◽  
Vol 32 (No. 1) ◽  
pp. 31-36 ◽  
Author(s):  
M. Králová ◽  
Z. Procházková ◽  
V. Svobodová ◽  
E. Mařicová ◽  
B. Janštová ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Storage Time ◽  
Near Infrared ◽  
Great Similarity ◽  
Calibration Models

We used the discriminant analysis of curd cheese during storage by Fourier transform near infrared spectroscopy method (FT-NIRs). Olomouc curd cheese samples were stored at 5 and at 20&deg;C during seven weeks. The spectra of samples were measured at the integration sphere in reflectance mode with the use of a compressive cell in the spectral range of 10&nbsp;000&ndash;4000 cm<sup>&ndash;1</sup> with 100 scans. Ten principal components were used for all the calibration models. Great similarity between the samples stored at 5 and 20&deg;C was found. Twelve samples stored at 20&deg;C for 1 week and 2 samples stored at 20&deg;C for 2 weeks were classified as samples stored at 5&deg;C. Different results were found out by comparing the storage time. 100% variability was described between the spectra scanned in different weeks of storage at 5&deg;C and 99.9% variability was obtained for the samples stored at 20&deg;C. Thus, the discriminant analysis of Olomouc curd cheese by FT-NIRs is a suitable method for the determination of ripening time. &nbsp;

Classification of Apple Using near Infrared Spectroscopy Based on Fuzzy Discriminant Analysis

2013 ◽  
Vol 710 ◽  
pp. 524-528 ◽  
Author(s):  
Xiao Hong Wu ◽  
Xing Xing Wan ◽  
Bin Wu ◽  
Fan Wu
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
K Nearest Neighbor ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Discriminant Information

Classification of apple is an important link in postharvest commercialization processing. To realize the non-destructive, rapid and effective discrimination of apple fruits, the near infrared reflectance spectra of four varieties of apples were collected using near infrared spectroscopy, reduced by principal component analysis (PCA) and used to extract the discriminant information by linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), fuzzy discriminant analysis (FDA) and Foley-Sammon discriminant analysis. Finally k-nearest neighbor finished the classification. The classification results showed that FDA could extract the discriminant information of NIR spectra more effectively, and achieved the highest classification accuracy.

scholarly journals Classification of cereal bars using near infrared spectroscopy and linear discriminant analysis

2013 ◽  
Vol 51 (2) ◽  
pp. 924-928 ◽  
Author(s):  
Anna Luiza Bizerra Brito ◽  
Lívia Rodrigues Brito ◽  
Fernanda Araújo Honorato ◽  
Márcio José Coelho Pontes ◽  
Liliana Fátima Bezerra Lira Pontes
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Linear Discriminant ◽  
Cereal Bars
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