Non-Destructive NIR Spectrometric Cultivar Discrimination of Castor Seeds Resulting from Breeding Programs

Santos, Maria Betania Hermenegildo dos; Gomes, A. A.; Vilar, Welma Thaíse Silva; Almeida, P. B. A. de; Milani, Máira; Nóbrega, Márcia B.M.; Medeiros, Everaldo S.; Galvão, Roberto Kawakami Harrop; Araújo, Mário César Ugulino de

doi:10.5935/0103-5053.20140068

Cited by 6 publications

(9 citation statements)

References 1 publication

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“…Further, identification of cultivars by these methods require seeds to be planted, which is time consuming since germination and development of the plant for at least a month are necessary before it can be identified by morphological traits or any other method. 10 So, there is a need for technologies which are robust, quick, non-destructive and reliable for the identification of different tomato cultivars.…”

Section: Introductionmentioning

confidence: 99%

Classification of different tomato seed cultivars by multispectral visible-near infrared spectroscopy and chemometrics

Shrestha

Deleuran

Gislum

2016

J. Spectral Imaging

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The feasibility of rapid and non-destructive classification of five different tomato seed cultivars was investigated by using visible and short-wave near infrared (Vis-NIR) spectra combined with chemometric approaches. Vis-NIR spectra containing 19 different wavelengths ranging from 375 nm to 970 nm were extracted from multispectral images of tomato seeds. Principal component analysis (PCA) was used for data exploration, while partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) were used to classify the five different tomato cultivars. The results showed very good classification accuracy for two independent test sets ranging from 94% to 100% for all tomato cultivars irrespective of chemometric methods. The overall classification error rates were 3.2% and 0.4% for the PLS-DA and SVM-DA calibration models, respectively. The results indicate that Vis-NIR spectra have the potential to be used for non-destructive discrimination of tomato seed cultivars with an opportunity to integrate them into plant genetic resource management, plant variety protection or registration programmes.

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Section: Introductionmentioning

confidence: 99%

Classification of different tomato seed cultivars by multispectral visible-near infrared spectroscopy and chemometrics

Shrestha

Deleuran

Gislum

2016

J. Spectral Imaging

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“…Among these markers, the most used are: RFLP (restriction fragment length polymorphism), AFLP (amplified fragment length polymorphism), RAPD (random amplification of polymorphic DNA) and microsatellites (ISSR—inter simple sequence repeats; SSR—simple sequence repeats) [ 12 , 14 ]. In general, the procedure for identifying some cultivars is carried out by planting the seed and, at least, one month is expected to pass so that, through their growth and development, their morphological identification occurs [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Infrared spectroscopy in the NIR region is considered a powerful tool for the quantitative and qualitative analysis of chemical and physical variables, and can be applied to samples of various types, such as from the pharmaceutical, polymer, petrochemical, food and agricultural industries [ 15 , 16 ]. However, in order to obtain results from the interactions with the analyte (seeds and grains), the development of chemometric models is required [ 12 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have demonstrated the potential of near-infrared (NIR) for the discrimination of seeds, namely non-destructive phenotyping using near-infrared spectroscopy developed classification models for two different commercial castor cultivars using PCA and SIMCA [ 15 ]; the separation of 4 soybean cultivars by near-infrared spectroscopy using PCA and HCA [ 18 ]; Fourier transform near-infrared spectroscopy combined with discriminant analyses was successfully utilized to classify two cultivars of sweetcorn seeds, based on the full-range wavelengths (1000–2500 nm) [ 19 ]; near-infrared spectroscopy and supervised pattern recognition techniques were used to classify five different macadamia cultivars based on intact nuts [ 20 ]; a near infrared (NIR) spectroscope as well as ED-XRF were used for the non-destructive discrimination of sesame seed origins (Korean, Chinese and Indian) [ 21 ]; and a discriminatory predictive model was used to determine the geographic origin of sesame seeds from Korea, China and other countries (India, Nigeria and Ethiopia) by NMR-based metabolomics focusing on polar metabolites [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Non-Destructive Genotyping of Cultivars and Strains of Sesame through NIR Spectroscopy and Chemometrics

et al. 2022

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The differentiation of cultivars is carried out by means of morphological descriptors, in addition to molecular markers. In this work, near-infrared spectroscopy (NIR) and chemometric techniques were used to develop classification models for two different commercial sesame cultivars (Sesamum indicum) and 3 different strains. The diffuse reflectance spectra were recorded in the region of 700 to 2500 nm. Based on the application of chemometric techniques: principal component analysis—PCA, hierarchical cluster analysis—HCA, k-nearest neighbor—KNN and the flexible independent modeling of class analogy—SIMCA, from the infrared spectra in the near region, it was possible to perform the genotyping of two sesame cultivars (BRS Seda and BRS Anahí), and to classify these cultivars with 3 different sesame strains, obtaining 100% accurate results. Due to the good results obtained with the implemented models, the potential of the methods for a possible realization of forensic, fast and non-destructive authentication, in intact sesame seeds was evident.

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“…SVM produced efficient and promising results in the discrimination of three types of tea (Chen et al 2007). PLSDA combined with NIR spectroscopy could be used as a rapid and non-destructive method to discriminate castor seeds for breeding programs (Santos et al 2014). Apart from the classification methods, the important feature selection could also highly influence the model performance.…”

Section: Introductionmentioning

confidence: 99%

Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods

et al. 2020

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The nutrition and quality of bamboo shoots from different species have a large variation, and traditional methods used for the classification of different bamboo shoot species are expensive and time-consuming. Here, the capability of near-infrared reflectance (NIR) spectroscopy to identify bamboo shoot species in a time-and cost-effective manner was examined. The NIR spectra of four bamboo shoot species were collected. Three classification models, a support vector machine (SVM), partial least squaresdiscriminant analysis (PLSDA), and random forest (RF), were calibrated. Several spectra pre-processing methods and their combination were trained before model calibration for the best classification model collection; each model was run 200 times for the calculation of the prediction error and model stability checking. The SVM model combined with the Det+2nd derivative had the best performance with an overall accuracy of 0.95. The use of less than 16% of the full-length NIR spectra produced a similar high accuracy of 0.91. Eight important regions,

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Non-Destructive NIR Spectrometric Cultivar Discrimination of Castor Seeds Resulting from Breeding Programs

Cited by 6 publications

References 1 publication

Classification of different tomato seed cultivars by multispectral visible-near infrared spectroscopy and chemometrics

Classification of different tomato seed cultivars by multispectral visible-near infrared spectroscopy and chemometrics

Non-Destructive Genotyping of Cultivars and Strains of Sesame through NIR Spectroscopy and Chemometrics

Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods

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