Use of near infrared spectra to identify cultivar in potato<i>(Solarium tuberosum)</i>crisps

Yee, Nigel; Bussell, W. T.; Coghill, G.G.

doi:10.1080/01140671.2006.9514404

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…On the other hand, NIRS prediction of reducing sugars, sucrose and total sugars was rather poor, as shown by the low r 2 pred values (Haase 2011). A study into the potential of NIRS to identify cultivars of potato used in the production of crisps has shown that discriminant analysis can separate the spectra into different classes corresponding to cultivars with a 93% success rate, suggesting that NIRS could be used to identify cultivars within single batches of potato crisps (Yee et al 2006). It has also been shown that NIRS can provide an accurate prediction of N concentrations, with a single global calibration, across a wide range of different plant species (Shepherd et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Use of NIRS for the rapid prediction of total N, minerals, sugars and starch in tropical root and tuber crops

Lebot

Malapa

Jung

2013

New Zealand Journal of Crop and Horticultural Science

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The objective of the present study was to test the robustness of near infrared spectroscopy (NIRS) for the prediction of total N content in underground storage organs across a diverse range of root and tuber crop varieties. Overall, 1096 accessions (acc.) from five different species (cassava 0112 acc., cocoyam 0117 acc., sweet potato 0225 acc., taro 0306 acc. and yams 0266 acc.) were chemically analysed for total N and minerals, as well as starch, sugars and cellulose. For validation of the models, 178 samples composed of the same five different species were collected in farmers' fields, at random. All spectra were taken over the wavelength range of 350Á2500 nm. Partial leastsquares (PLS1) regression technique was used to develop predictive models. Their comparison with the chemical values allowed the establishment of equations of calibration. In terms of predictive performance, the equation for total N should be considered as very good with a r

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Section: Introductionmentioning

confidence: 99%

Use of NIRS for the rapid prediction of total N, minerals, sugars and starch in tropical root and tuber crops

Lebot

Malapa

Jung

2013

New Zealand Journal of Crop and Horticultural Science

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“…Previously, various studies used a variety of preprocessing and chemometric approaches to differentiate plant species. Yee et al [ 29 ] employed NIR spectra in conjunction with LDA to discriminate potato tuber varieties, with a classification accuracy of 93%. Chen et al [ 30 ] used SVM to differentiate three tea varietals.…”

Section: Resultsmentioning

confidence: 99%

Discrimination of Brassica juncea Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods

Sohn

Subramanian²,

Oh³

et al. 2022

IJMS

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Brown mustard (Brassica juncea (L.) is an important oilseed crop that is mostly used to produce edible oils, industrial oils, modified lipids and biofuels in subtropical nations. Due to its higher level of commercial use, the species has a huge array of varieties/cultivars. The purpose of this study is to evaluate the use of visible near-infrared (Vis-NIR) spectroscopy in combination with multiple chemometric approaches for distinguishing four B. juncea varieties in Korea. The spectra from the leaves of four different growth stages of four B. juncea varieties were measured in the Vis-NIR range of 325–1075 nm with a stepping of 1.5 nm in reflectance mode. For effective discrimination, the spectral data were preprocessed using three distinct approaches, and eight different chemometric analyses were utilized. After the detection of outliers, the samples were split into two groups, one serving as a calibration set and the other as a validation set. When numerous preprocessing and chemometric approaches were applied for discriminating, the combination of standard normal variate and deep learning had the highest classification accuracy in all the growth stages achieved up to 100%. Similarly, few other chemometrics also yielded 100% classification accuracy, namely, support vector machine, generalized linear model, and the random forest. Of all the chemometric preprocessing methods, Savitzky–Golay filter smoothing provided the best and most convincing discrimination. The findings imply that chemometric methods combined with handheld Vis-NIR spectroscopy can be utilized as an efficient tool for differentiating B. juncea varieties in the field in all the growth stages.

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“…LDA combined with principal component analysis (PCA) as a pre-processing method had the best classification accuracies. Yee et al(2006) used NIR spectra to identify the potato tuber variety based on LDA pattern recognition techniques. The success rate in separating the spectra into respective classes was 93%.…”

Section: Introductionmentioning

confidence: 99%

On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy

Peng

et al. 2009

J. Zhejiang Univ. Sci. B

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Abstract:The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207). In this study, 82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode. Discriminant models were developed using principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least squares (DPLS) regression methods. After outliers detection, the samples were randomly split into two sets, one used as a calibration set (n=82) and the remaining samples as a validation set (n=82). When predicting the variety of the samples in validation set, the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%. The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (R c )=0.920, root mean square errors of calibration=0.196, and root mean square errors of prediction=0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.

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Use of near infrared spectra to identify cultivar in potato(Solarium tuberosum)crisps

Cited by 5 publications

References 11 publications

Use of NIRS for the rapid prediction of total N, minerals, sugars and starch in tropical root and tuber crops

Use of NIRS for the rapid prediction of total N, minerals, sugars and starch in tropical root and tuber crops

Discrimination of Brassica juncea Varieties Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics Methods

On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy

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