Application of partial least squares near-infrared spectral classification in diabetic identification

Yan, Wenjuan; Yang, Ming; He, Guojin; Lin, Qingyu; Li, Gang

doi:10.1117/12.2070592

Cited by 1 publication

(1 citation statement)

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“…The wolfberries from different geographical origins had similar spectral patterns and all had absorption peaks at approximately 995, 1200 and 1465 nm. The absorption peak near 995 nm was attributed to the second vibration of N-H bonds in proteins or amino acids [ 30 – 31 ]. The absorption peak near 1200 nm was attributed to the secondary stretching vibration of C-H bonds in starch, proteins or lipids [ 32 – 33 ].…”

Section: Resultsmentioning

confidence: 99%

Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries

et al. 2017

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Near-infrared (874–1734 nm) hyperspectral imaging (NIR-HSI) technique combined with chemometric methods was used to trace origins of 1200 Chinese wolfberry samples, which from Ningxia, Inner Mongolia, Sinkiang and Qinghai in China. Two approaches, named pixel-wise and object-wise, were investigated to discriminative the origin of these Chinese wolfberries. The pixel-wise classification assigned a class to each pixel from individual Chinese wolfberries, and with this approach, the differences in the Chinese wolfberries from four origins were reflected intuitively. Object-wise classification was performed using mean spectra. The average spectral information of all pixels of each sample in the hyperspectral image was extracted as the representative spectrum of a sample, and then discriminant analysis models of the origins of Chinese wolfberries were established based on these average spectra. Specifically, the spectral curves of all samples were collected, and after removal of obvious noise, the spectra of 972–1609 nm were viewed as the spectra of wolfberry. Then, the spectral curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were established based on the full-band spectra, the extracted characteristic wavelengths from loadings of principal component analysis (PCA) and 2nd derivative spectra, respectively. Among these models, the recognition accuracies of the calibration set and prediction set of the ELM model based on extracted characteristic wavelengths from loadings of PCA were higher than 90%. The model not only ensured a high recognition rate but also simplified the model and was conducive to future rapid on-line testing. The results revealed that NIR-HSI combined with PCA loadings-ELM could rapidly trace the origins of Chinese wolfberries.

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