Development of Detection System with Low Predictive Errors for Determining Vitamin C Content of Indian Jujube  
Shih-Hao Hua  
Graduate Institute of Precision Engineering, National Chung Hsing University, No.145, Xingda Rd., South Dist., Taichung 402, Taiwan; [EMAIL_ADDRESS] (S.-H.H.); [EMAIL_ADDRESS] (H.-C.H.)  
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Hsun-Ching Hsu  
Graduate Institute of Precision Engineering, National Chung Hsing University, No.145, Xingda Rd., South Dist., Taichung 402, Taiwan; [EMAIL_ADDRESS] (S.-H.H.); [EMAIL_ADDRESS] (H.-C.H.)  
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Pin Han  
Graduate Institute of Precision Engineering, National Chung Hsing University, No.145, Xingda Rd., South Dist., Taichung 402, Taiwan; [EMAIL_ADDRESS] (S.-H.H.); [EMAIL_ADDRESS] (H.-C.H.)  
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The most typically used algorithms in NDTs are multiple linear regression (MLR) and partial least squares (PLS) regression and the calculations are performed by substituting the energy absorbed by the fruits' flesh (which is derived from the intensity of the reflected light waves) into these algorithms. In MLR, the intensities of a few specified wavelengths are linearly combined to predict some variables. However, in spectroscopic analysis, the presence of multicollinearity between the variables will negatively affect the MLR performance [9; 10]. [PERSON] et al. [11] indicated that the calibrated and predicted results of the MLR model can differ significantly, which indicates that MLR predictions are not reliable. PLS extracts the orthogonal factors with the maximum predictive power, which may then be used to predict the variable. Many previous studies have employed PLS models to predict vitamin C content. For example, [PERSON] et al. [12] used a PLS model to determine vitamin C content based on Fourier transform near-infrared (FT-NIR) and FT-Raman spectra. [PERSON] [13] used PLS regression to construct a model for quantifying the vitamin C content of naval oranges via NIR diffuse reflectance spectroscopy. In addition, [PERSON] et al. [14] and [PERSON] et al. [15] used PLS models to calculate fruit soluble solid content (SSC) and fruit flesh firmness. Based on the results of these studies, we decided to use the PLS algorithm to analyze our spectroscopic data.  
According to the current market needs, a high accuracy, low prediction error and low-cost nondestructive detection system has great potential. The novelty of this study lies in the construction of a parallel-polarized NIR nonconstructive detection system for determining the vitamin C content in Indian jujube. In this study, the OH and CH\({}_{2}\) bonds absorb the parallel-polarized waves substantially [16] and light sources reduce the reflectance of fruit skin and increase detection accuracy at the Brewster's angle (\(\theta_{\text{B}}\)) of incidence [17].  
In the present study, the light sources were constructed by using five NIR parallel-polarized wavelength combinations and a Ge photodetector, which greatly reduced the production cost of equipment. Therefore, the optoelectronic detection system achieves high accuracy, a low prediction error and low production cost.