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.

Prediction of Soluble Solids Content and Post-Storage Internal Quality of Bulida Apricots Using near Infrared Spectroscopy

2007 ◽  
Vol 15 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Marena Manley ◽  
Elizabeth Joubert ◽  
Lindie Myburgh ◽  
Ester Lotz ◽  
Martin Kidd
Keyword(s):  
Cold Storage ◽  
Regression Models ◽  
Near Infrared ◽  
Storage Period ◽  
Multivariate Adaptive Regression Splines ◽  
Soluble Solids ◽  
Internal Quality ◽  
Soluble Solids Content ◽  
Classification Rate ◽  
Solids Content

The development of internal breakdown of South African Bulida apricots during cold storage, rendering the fruit unsuitable for canning, causes significant post-harvest losses. Regression models to predict internal post-storage quality using near infrared (NIR) spectroscopy and multivariate classification techniques were developed using NIR spectra of the intact fruit collected prior to storage and subjective quality evaluations performed after a cold storage period of four weeks. A correct classification rate of 69% was obtained using multivariate adaptive regression splines (MARS) compared to 50% obtained by soft independent modelling by class analogy (SIMCA). NIR regression models developed for soluble solids content (SSC) of intact fruit as well as for direct NIR measurements on the exposed fruit tissue gave similar results, thus confirming sufficient NIR light penetration into the intact fruit. The best prediction results were obtained when two spectral measurements per fruit (one on each half of the fruit), compared to single measurements, were used.

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.

A simple multiple linear regression model in near infrared spectroscopy for soluble solids content of pomegranate arils based on stability competitive adaptive re-weighted sampling

2021 ◽  
pp. 096703352098236
Author(s):  
Zhaoqiong Jiang ◽  
Yiping Du ◽  
Fangping Cheng ◽  
Feiyu Zhang ◽  
Wuye Yang ◽  
...  
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soluble Solids ◽  
Pls Regression ◽  
Variable Selection Method ◽  
Solids Content ◽  
Pomegranate Arils ◽  
Mlr Model

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.

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