NIR spectroscopy fruit quality detection algorithm based on the least angle regression model

Author(s):  
Songjian Dan
2018 ◽  
Author(s):  
Yuda Hadiwijaya

Fruit quality detection using near-infraread spectrometer is fast and it does not damage the fruit, hence the fruit is still marketable. The aim of this research focused on analyzing quality component of ridge gourd during storage using near-infrared spectrometer. The research was conducted on March to July 2017 at the Laboratory Plant Production Technology of Horticulture Division of Agriculture Faculty of Padjadjaran University, Jatinangor. Ridge gourds were harvested at the same maturity stage from the orchad, then stored at 5 and 10 days. The method used in this research was multivariate data analysis using Unscrambler software (version 7.51, CAMO, Oslo, Norway). The data acquisition was taken using portable near-infrared (NIR) spectrometer (NirVana AG410, Integrated Spectronics Pty, Ltd, Australia) with wavelength range of 600-1100 nm and stored as absorbance spectra and pretreated by secondderivatives spectra using ISIS software (Integrated Spectronics Pty, Ltd, Australia). The results showed that non-destrucive method using near-infrared spectrometer was able to measure ridge gourd fruit quality component such as, total dissolved solid, moisture content, firmness and color values.


2019 ◽  
Vol 56 (9) ◽  
pp. 090003
Author(s):  
邓博涵 Deng Bohan ◽  
陈嘉豪 Chen Jiahao ◽  
胡孟晗 Hu Menghan ◽  
许文平 Xu Wenping ◽  
张才喜 Zhang Caixi

2021 ◽  
pp. 096703352098236
Author(s):  
Zhaoqiong Jiang ◽  
Yiping Du ◽  
Fangping Cheng ◽  
Feiyu Zhang ◽  
Wuye Yang ◽  
...  

The objective of this study was to develop a multiple linear regression (MLR) model using near infrared (NIR) spectroscopy combined with chemometric techniques for soluble solids content (SSC) in pomegranate samples at different storage periods. A total of 135 NIR diffuse reflectance spectra with the wavelength range of 950-1650 nm were acquired from pomegranate arils. Based upon sampling error profile analysis (SEPA), outlier diagnosis was conducted to improve the stability of the model, and four outliers were removed. Several pretreatment and variable selection methods were compared using partial least squares (PLS) regression models. The overall results demonstrated that the pretreatment method of the first derivative (1D) was very effective and the variable selection method of stability competitive adaptive re-weighted sampling (SCARS) was powerful for extracting feature variables. The equilibrium performance of 1D-SCARS-PLS regression model for ten times was similar to 1D-PLS regression model, so that the advantage of wavelength selection was inconspicuous in PLS regression model. However, the number of variables selected by 1D-SCARS was less to 9, which was enough to establish a simple MLR model. The performance of MLR model for SSC of pomegranate arils based on 1D-SCARS was receivable with the root-mean-square error of calibration set (RMSEC) of 0.29% and prediction set (RMSEP) of 0.31%. This strategy combining variable selection method with MLR may have a broad prospect in the application of NIR spectroscopy due to its simplicity and robustness.


Sign in / Sign up

Export Citation Format

Share Document