Near Infrared Applications in the Quality Control of Seed Cotton

Gaitán-Jurado, A.J.; García-Molina, María; Peña-Rodríguez, Francisco; Ortiz-Somovilla, Víctor

doi:10.1255/jnirs.806

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…By scanning the NIR spectrum of a sample, the characteristics of hydrogen-containing groups of organic molecules in the sample can be obtained. [10][11][12] The SupNIR-1550 fiber optic spectrometer, a raster scanning product of Focused Photonics (Hangzhou) Inc., is adopted in the experiment. Its wavelength range is 1000-2500 nm, its sampling spot is greater than 30 mm, and its signal-to-noise ratio is 30,000:1, with an optical fiber.…”

Section: Collection Of Recycled Textile Samples Test Methodsmentioning

confidence: 99%

Qualitative classification of waste textiles based on near infrared spectroscopy and the convolutional network

Liu

Wei

2019

Textile Research Journal

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The recycling of waste textiles has become a growth point for the sustainable development of the textile and clothing industry. In addition, sorting is a key link in the follow-up recycling process. Since different fabrics are required to be processed by different technologies, manual sorting not only takes time and effort but also cannot achieve accurate and reliable classification. Based on the analysis of near infrared spectroscopy, the theory and methods of deep learning are used for the qualitative classification of waste textiles in order to complete the automatic fabric composition recognition in the sorting process. Firstly, a standard sample set is established by waveform clipping and normalization, and a Textile Recycling Net deep web suitable for near infrared spectroscopy is established. Then, a pixilated layer is used to facilitate the deep learning of features, and the multidimensional features of the spectrum are extracted by using the multi-layer convolutional and pooling layers. Finally, the softmax classifier is adopted to complete the qualitative classification. Experimental results show that the convolutional network classification method using normalized and pixelated near infrared spectroscopy can realize the automatic classification of several common textiles, such as cotton and polyester, and effectively improve the detection level and speed of fabric components.

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Section: Collection Of Recycled Textile Samples Test Methodsmentioning

confidence: 99%

Qualitative classification of waste textiles based on near infrared spectroscopy and the convolutional network

Liu

Wei

2019

Textile Research Journal

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“…Gamble and Foulk 17 built the partial least squares (PLS) models of six botanical trash types using fluores cence spectroscopy and the models of leaves and hull were capable of predicting individual trash component with a high degree of confidence. GaitánJurado et al 18 deter mined the moisture content and impurity level of SC using NIR spectra. For moisture, the best model was obtained using PLS regression method, the first derivate, drying method, standard normal variate (SNV), and detrending as the pretreatment method.…”

Section: Introductionmentioning

confidence: 99%

The quantitative detection of botanical trashes contained in seed cotton with near infrared spectroscopy method

Zhou

Liang

2022

Journal of Engineered Fibers and Fabrics

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This study is performed to investigate the potential of near infrared (NIR) spectroscopy for the detection of botanical trashes content of seed cotton harvested by cotton-picker (SCHCP). Large quantity of trashes become comingled with cotton fiber in the harvesting process, especially when the cotton is harvested with cotton-picker. In China, trashes content of seed cotton (SC) has to be detected when farmers sell the SC to ginneries because trashes reduce the prices of SC and it should be deducted from the whole weight. The conventional instrumental method used to detect the trashes content of SC, ginning and trashes analysis, is complex and time consuming. In this study, 353 SC samples were collected from three ginneries, the NIR spectra bands from 12,000 to 4000 cm−1 were collected with the FT-NIR spectrometer Nexus. Models between NIR spectra and the trashes contents of these SC samples have been developed with the method of partial least square regression (PLSR), bands of 12,000–4000 cm−1, multiplicative signal correction (MSC) was used to eliminate the negative effects caused by sample shapes, second derivative spectra were used to eliminate the translation and the rotation in the spectral baseline. And the parameters of optimized model: R2 is up to 0.985 (calibration set) and 0.973 (prediction set), RMSEC is as low as 0.072 g and RMSEP is 0.158 g. Results of ANOVA also certified the trashes contents calculated with the models are consistent with the actual trashes contents.

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“…Seed research has been successful in evaluating quality, such as soybeans, cotton and tomato (Bazoni et al, 2017;Gaitán-Jurado et al, 2008;Huang et al, 2013;Shrestha et al, 2016), viability in maize and spinach (Ambrose et al, 2016;Shetty et al, 2012), composition and prediction of phytic acid in Vigna radiata (Pande and Mishra, 2015), nitrogen content in Vigna unguiculata (Ishikawa et al, 2017), oil composition in sunflower and canola (Grunvald et al, 2014;Rossato et al, 2013), as well as the classification of genotypes in cotton, barley, castor beans and maize (Cui et al, 2012;Jia et al, 2015;Ringsted et al, 2016;Santos et al, 2014;Soares et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Use of near infrared spectroscopy in cotton seeds physiological quality evaluation

et al. 2020

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This study aimed to evaluate the near-infrared spectroscopy potential in analyzing the quality of cottonseed regarding different physiological quality levels, noting the need for faster techniques and tools to aid decision making. It was used eight samples of cottonseed with and without lint, presenting different physiological quality. The “high” (lots 1, 4, 5, 6 and 7) and “low” (lots 2, 3 and 8) vigor levels were defined based on vigor tests carried out and on the Normative Instruction 45/2013. The near infrared spectroscopy spectra was obtained from four types of sample preparations: whole seeds, cut in a half, without tegument and grounded seeds. Using the spectra and the grouping of lots in high and low vigor, cross validation models were optimized, built using the PLS - DA method, making it possible to predict seed classes. Grounded seeds were the best type of sample preparation, with 95% of correct predictions for high vigor seeds and 100% of low vigor (both for seeds with lint) and with 100% correct predictions for high vigor seeds and 91.7% low vigor (without lint).

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Near Infrared Applications in the Quality Control of Seed Cotton

Abstract: Ifapa-centro 'alameda del obispo' (postharvest and food technology area), avda. Menéndez pidal s/n, apdo. correos 3

Cited by 7 publications

References 13 publications

Qualitative classification of waste textiles based on near infrared spectroscopy and the convolutional network

Qualitative classification of waste textiles based on near infrared spectroscopy and the convolutional network

The quantitative detection of botanical trashes contained in seed cotton with near infrared spectroscopy method

Use of near infrared spectroscopy in cotton seeds physiological quality evaluation

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