Development of NIRS models to predict composition of enzymatically processed sweetpotato

Diaz, Joscelin T.; Veal, Matthew W.; Chinn, Mari S.

doi:10.1016/j.indcrop.2014.05.012

Cited by 15 publications

(8 citation statements)

References 17 publications

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“…Previous research confirmed that Vis-NIR measurements are useful for the prediction of blueberry quality in terms of ripening indexes (soluble solids content and firmness) and presence of compounds with functional properties (total anthocyanins, total flavonoids, total polyphenols and ascorbic acid) (Guidetti et al, 2008). The use of NIR technique for predicting some particular components of fresh/processed food was recently confirmed by different authors (Blanco-Diaz, Del Rio-Celestino, Martinez-Valdivieso, & Font, 2014;Diaz, Veal, & Chinn, 2014;Georgieva et al, 2013;Sanchez et al, 2014). Though investigations have been conducted on polyphenolic analysis using NIR for some fruit (Ferrer-Gallego, Hernandez-Hierro, Rivas-Gonzalo, & Escribano-Bailon, 2011;Inacio, de Lima, Lopes, Pessoa, & Teixeira, 2013), to date it has not been used to estimate phenolic compounds in fruit juice treated with cold plasma.…”

Section: Introductionmentioning

confidence: 78%

Gas phase plasma impact on phenolic compounds in pomegranate juice

et al. 2016

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Section: Introductionmentioning

confidence: 78%

Gas phase plasma impact on phenolic compounds in pomegranate juice

et al. 2016

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“…These values confirm that the equations were moderately successful. It was reported that moisture content affects hydrogen content within the sample and that, as a result, band width and position can change [ 7 ]. Moreover, the moisture content perhaps changed during sample preparation and scanning from external factors such as temperature, and the humidity of the environment was previously reported to affect the accuracy of moisture determination [ 30 ].…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, it is necessary to analyze the nutrient composition of corn stover and wheat straw materials in a timely and accurate manner. The composition of roughage usually varies by harvest time, storage condition, and processing method [ 7 ]. However, the analysis of straw chemical composition is time-consuming and costly by conventional analytical techniques, especially when a large number of samples are required.…”

Section: Introductionmentioning

confidence: 99%

Measurements of Chemical Compositions in Corn Stover and Wheat Straw by Near-Infrared Reflectance Spectroscopy

Guo

Dai

Yan

et al. 2021

Animals

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Rapid, non-destructive methods for determining the biochemical composition of straw are crucial in ruminant diets. In this work, ground samples of corn stover (n = 156) and wheat straw (n = 135) were scanned using near-infrared spectroscopy (instrument NIRS DS2500). Samples were divided into two sets, with one set used for calibration (corn stover, n = 126; wheat straw, n = 108) and the remaining set used for validation (corn stover, n = 30; wheat straw, n = 27). Calibration models were developed utilizing modified partial least squares (MPLS) regression with internal cross validation. Concentrations of moisture, crude protein (CP), and neutral detergent fiber (NDF) were successfully predicted in corn stover, and CP and moisture were in wheat straw, but other nutritional components were not predicted accurately when using single-crop samples. All samples were then combined to form new calibration (n = 233) and validation (n = 58) sets comprised of both corn stover and wheat straw. For these combined samples, the CP, NDF, and ADF were predicted successfully; the coefficients of determination for calibration (RSQC) were 0.9625, 0.8349, and 0.8745, with ratios of prediction to deviation (RPD) of 6.872, 2.210, and 2.751, respectively. The acid detergent lignin (ADL) and moisture were classified as moderately useful, with RSQC values of 0.7939 (RPD = 2.259) and 0.8342 (RPD = 1.868), respectively. Although the prediction of hemicellulose was only useful for screening purposes (RSQC = 0.4388, RPD = 1.085), it was concluded that NIRS is a suitable technique to rapidly evaluate the nutritional value of forage crops.

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“…The PLSR model showed acceptable accuracy ( R 2 P = 0.70) [ 96 ]. Compared with PLSR, MLR showed better performance in predicting potato and sweet potato starch, with the R 2 P as high as 0.97 [ 125 , 126 ]. The FMCIA-MLR and FMCIA-PLSR developed using six feature variables obtained similar results with R 2 P of 0.962 and 0.963 for tuber DMC and SC, respectively.…”

Section: Applications For Tuber Quality Assessmentmentioning

confidence: 99%

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

Xue

2021

Foods

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Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.

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Development of NIRS models to predict composition of enzymatically processed sweetpotato

Cited by 15 publications

References 17 publications

Gas phase plasma impact on phenolic compounds in pomegranate juice

Gas phase plasma impact on phenolic compounds in pomegranate juice

Measurements of Chemical Compositions in Corn Stover and Wheat Straw by Near-Infrared Reflectance Spectroscopy

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality

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