Robust model of fresh jujube soluble solids content with near-infrared (NIR) spectroscopy

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.

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

Journal of Innovative Optical Health Sciences ◽

10.1142/s179354581350065x ◽

2014 ◽

Vol 07 (06) ◽

pp. 1350065 ◽

Cited By ~ 6

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.

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Simultaneous detection of total antioxidant capacity and total soluble solids content by Fourier transform near-infrared (FT-NIR) spectroscopy: A quick and sensitive method for on-site analyses of apples

Food Control ◽

10.1016/j.foodcont.2016.01.026 ◽

2016 ◽

Vol 66 ◽

pp. 27-37 ◽

Cited By ~ 42

Author(s):

Matthias Schmutzler ◽

Christian W. Huck

Keyword(s):

Fourier Transform ◽

Total Antioxidant Capacity ◽

Near Infrared ◽

Simultaneous Detection ◽

Nir Spectroscopy ◽

Soluble Solids ◽

Soluble Solids Content ◽

Total Antioxidant ◽

Solids Content ◽

Sensitive Method

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Influences of Detection Position and Double Detection Regions on Determining Soluble Solids Content (SSC) for Apples Using On-line Visible/Near-Infrared (Vis/NIR) Spectroscopy

Food Analytical Methods ◽

10.1007/s12161-019-01530-7 ◽

2019 ◽

Vol 12 (9) ◽

pp. 2078-2085 ◽

Cited By ~ 1

Author(s):

Xiao Xu ◽

Jiancan Mo ◽

Lijuan Xie ◽

Yibin Ying

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Soluble Solids ◽

Soluble Solids Content ◽

On Line ◽

Solids Content

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Evaluation of Quality Parameters of Apple Juices Using Near-Infrared Spectroscopy and Chemometrics

Journal of Spectroscopy ◽

10.1155/2018/5191283 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Katarzyna Włodarska ◽

Igor Khmelinskii ◽

Ewa Sikorska

Keyword(s):

Near Infrared ◽

Cross Validation ◽

Nir Spectroscopy ◽

Titratable Acidity ◽

Quality Parameters ◽

Soluble Solids ◽

Soluble Solids Content ◽

Model Optimization ◽

Important Quality ◽

Solids Content

Near-infrared (NIR) spectra were recorded for commercial apple juices. Analysis of these spectra using partial least squares (PLS) regression revealed quantitative relations between the spectra and quality- and taste-related properties of juices: soluble solids content (SSC), titratable acidity (TA), and the ratio of soluble solids content to titratable acidity (SSC/TA). Various spectral preprocessing methods were used for model optimization. The optimal spectral variables were chosen using the jack-knife-based method and different variants of the interval PLS (iPLS) method. The models were cross-validated and evaluated based on the determination coefficients (R2), root-mean-square error of cross-validation (RMSECV), and relative error (RE). The best model for the prediction of SSC (R2 = 0.881, RMSECV = 0.277 °Brix, and RE = 2.37%) was obtained for the first-derivative preprocessed spectra and jack-knife variable selection. The optimal model for TA (R2 = 0.761, RMSECV = 0.239 g/L, and RE = 4.55%) was obtained for smoothed spectra in the range of 6224–5350 cm−1. The best model for the SSC/TA (R2 = 0.843, RMSECV = 0.113, and RE = 5.04%) was obtained for the spectra without preprocessing in the range of 6224–5350 cm−1. The present results show the potential of the NIR spectroscopy for screening the important quality parameters of apple juices.

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THE DEVELOPMENT OF PORTABLE DETECTOR FOR APPLE' S SOLUBLE SOLIDS CONTENT BASED ON VISIBLE AND NEAR INFRARED SPECTRUM

INMATEH Agricultural Engineering ◽

10.35633/inmateh-62-29 ◽

2020 ◽

pp. 277-288

Author(s):

Fa Peng ◽

ShuangXi Liu ◽

Hao Jiang ◽

XueMei Liu ◽

JunLin Mu ◽

...

Keyword(s):

Infrared Spectrum ◽

Prediction Model ◽

Spectral Data ◽

Near Infrared ◽

Mobile App ◽

Partial Least Square ◽

Soluble Solids ◽

Soluble Solids Content ◽

Solids Content ◽

Near Infrared Spectrum

In order to detect the soluble solids content of apples quickly and accurately, a portable apple soluble solids content detector based on USB2000 + micro spectrometer was developed. The instrument can communicate with computer terminal and mobile app through network port, Bluetooth and other ways, which can realize the rapid acquisition of apple spectral information. Firstly, the visible / near-infrared spectrum data and soluble solids content information of 160 apple samples were collected; secondly, the spectral data preprocessing methods were compared, and the results showed that the prediction model of sugar content based on partial least square (PLS) method after average smoothing preprocessing was accurate. The correlation coefficient (RP) and root mean square error (RMSEP) of the prediction model were 0.902 and 0.589 ° Brix, respectively. Finally, on the basis of average smoothing preprocessing, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the wavelength of spectral data, and PLS model was constructed based on the selected 17 characteristic wavelengths, which can increase the accuracy of soluble solids content prediction model, increase the RP to 0.912, and reduce RMSEP to 0.511 ° Brix. The portable visible / near infrared spectrum soluble solids prediction model based on the instrument and method has high accuracy, and the detector can quickly and accurately measure the soluble solids content of apple.

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