scholarly journals Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Lin Zhang ◽  
Baohua Zhang ◽  
Jun Zhou ◽  
Baoxing Gu ◽  
Guangzhao Tian
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Scatter Correction ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Biological Variability ◽  
Wavelength Selection ◽  
Calibration Models ◽  
Solids Content

Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.

Nondestructive Detection of Soluble Solids Content of Nanfeng Mandarin Orange Using VIS-NIR Spectroscopy

2011 ◽  
Vol 361-363 ◽  
pp. 1634-1637 ◽  
Author(s):  
Lu Zhang ◽  
Long Xue ◽  
Mu Hua Liu ◽  
Jing Li
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Rapid Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Soluble Solids ◽  
Soluble Solids Content ◽  
Solids Content ◽  
Mandarin Orange

This study demonstrated how VIS-NIR spectroscopy can be used in the quantitative, noninvasive probing of soluble solids content (SSC) of mandarin orange. Total 197 mandarin oranges were divided into calibration set (133 samples) and prediction set (64 samples). Multiple scatter correction (MSC) was used to preprocess the collected visible and near infrared (Vis-NIR) spectra (350-1800nm) of mandarin orange. Partial least square (PLS), interval partial least square (IPLS) and synergy interval partial least square (SIPLS) methods were applied for constructing predictive models of SSC. Experimental results showed that the optimal SIPLS model obtained with 10 PLS components and the optimal combinations of intervals were number 5,7,8,9. The correlation coefficient (r) between the predicted and actual SSC was 0.9265 and 0.8577 for calibration and prediction set, respectively. The root mean square error of calibration (RMSEC) and prediction (RMSEP) set was 0.4890 and 0.7113, respectively. In conclusion, the combination of Vis-NIR spectroscopy and SIPLS methods can be used to provide a technique of noninvasive, convenient and rapid analysis for SSC in fruit.

scholarly journals Online quantitative analysis of soluble solids content in navel oranges using visible-near infrared spectroscopy and variable selection methods

2014 ◽  
Vol 07 (06) ◽  
pp. 1350065 ◽  
Author(s):  
Yande Liu ◽  
Yanrui Zhou ◽  
Yuanyuan Pan
Keyword(s):  
Quantitative Analysis ◽  
Variable Selection ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Internal Quality ◽  
Soluble Solids Content ◽  
Selection Methods ◽  
Calibration Models ◽  
Solids Content

Variable selection is applied widely for visible-near infrared (Vis-NIR) spectroscopy analysis of internal quality in fruits. Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content (SSC) in navel oranges. Moving window partial least squares (MW-PLS), Monte Carlo uninformative variables elimination (MC-UVE) and wavelet transform (WT) combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges. The performances of these methods were compared for modeling the Vis-NIR data sets of navel orange samples. Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation coefficient (r) of 0.89 and lower root mean square error of prediction (RMSEP) of 0.54 at 5 fruits per second. It concluded that Vis-NIR spectroscopy coupled with WT-MC-UVE may be a fast and effective tool for online quantitative analysis of SSC in navel oranges.

Quantitative detection of soluble solids content, pH, and total phenol in Cabernet Sauvignon grapes based on near infrared spectroscopy

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yijia Luo ◽  
Juan Dong ◽  
Xuewei Shi ◽  
Wenxia Wang ◽  
Zhuoman Li ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Soluble Solids ◽  
Cabernet Sauvignon ◽  
Soluble Solids Content ◽  
Solids Content

Abstract Determination of Cabernet Sauvignon grapes quality plays an important role in commercial processing. In this research, a rapid approach based on near infrared spectroscopy was proposed to the determination of soluble solids content (SSC), pH, and total phenol content (TPC) in entire bunches of Cabernet Sauvignon grapes. Standardized normal variate (SNV) and competitive adaptive weighted sampling (CARS), genetic algorithm (GA), and synergy interval partial least squares (si-PLS) were used to optimize the spectral data. With optimal combination input, the prediction accuracy of partial least squares regression (PLSR) and support vector regression (SVR) models was compared. The results showed that these models based on variable optimization method could predict well the SSC, pH, and TPC of Cabernet Sauvignon grapes. The correlation coefficient of prediction for SSC, pH, and TPC had reached more than 0.85. This work provides an alternative to analyze the chemical parameters in whole bunch of Cabernet Sauvignon grape.

Sign in / Sign up

Export Citation Format

Share Document