scholarly journals Rapid Nondestructive Detection of Water Content and Granulation in Postharvest “Shatian” Pomelo Using Visible/Near-Infrared Spectroscopy

Biosensors ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 41
Author(s):  
Sai Xu ◽  
Huazhong Lu ◽  
Christopher Ference ◽  
Guangjun Qiu ◽  
Xin Liang
Keyword(s):  
Feature Extraction ◽  
Data Processing ◽  
Water Content ◽  
Near Infrared ◽  
Internal Quality ◽  
Nondestructive Detection ◽  
Information Mining ◽  
Quality Detection ◽  
Spectral Signal ◽  
Validation Set

Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small, thin-peeled fruit, though less so for large, thick-peeled fruit due to a weak spectral signal resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. “Shatian” pomelo is a traditional Chinese large, thick-peeled fruit, and granulation and water loss are two major internal quality factors that influence its storage quality. However, there is no efficient, nondestructive detection method for measuring these factors. Thus, the VIS/NIR spectral signal detection of 120 pomelo samples during storage was performed. Information mining (singular sample elimination, data processing, feature extraction) and modeling were performed in different ways to construct the optimal method for achieving an accurate detection. Our results showed that the water content of postharvest pomelo was optimally detected using the Savitzky–Golay method (SG) plus the multiplicative scatter correction method (MSC) for data processing, genetic algorithm (GA) for feature extraction, and partial least squares regression (PLSR) for modeling (the coefficient of determination and root mean squared error of the validation set were 0.712 and 0.0488, respectively). Granulation degree was best detected using SG for data processing and PLSR for modeling (the detection accuracy of the validation set was 100%). Additionally, our research showed a weak relationship between the pomelo water content and granulation degree, which provided a reference for the existing debates. Therefore, our results demonstrated that VIS/NIR combined with optimal information mining and modeling methodswas feasible for determining the water content and granulation degree of postharvest pomelo, and for providing references for the nondestructive internal quality detection of other large, thick-peeled fruits.

scholarly journals An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 80
Author(s):  
Sai Xu ◽  
Huazhong Lu ◽  
Christopher Ference ◽  
Qianqian Zhang
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Data Processing ◽  
Water Content ◽  
Information Fusion ◽  
Nir Spectroscopy ◽  
Internal Quality ◽  
Source Information ◽  
Accuracy Improvement ◽  
Quality Detection

The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.

scholarly journals Quantification of Water, Protein and Soluble Sugar in Mulberry Leaves Using a Handheld Near-Infrared Spectrometer and Multivariate Analysis

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4439 ◽  
Author(s):  
Yue Ma ◽  
Guo-Zheng Zhang ◽  
Sedjoah Aye-Ayire Rita-Cindy
Keyword(s):  
Root Mean Square Error ◽  
Water Content ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Crude Protein ◽  
Near Infrared ◽  
Soluble Sugar ◽  
Mean Square ◽  
Mulberry Leaves ◽  
Validation Set

Mulberry (Morus alba L.) leaves are not only used as the main feed for silkworms (Bombyx mori) but also as an added feed for livestock and poultry. In order to rapidly select high-quality mulberry leaves, a hand-held near-infrared (NIR) spectrometer combined with partial least squares (PLS) regression and wavelength optimization methods were used to establish a predictive model for the quantitative determination of water content in fresh mulberry leaves, as well as crude protein and soluble sugar in dried mulberry leaves. For the water content in fresh mulberry leaves, the R-square of the calibration set ( R C 2 ), R-square of the cross-validation set ( R C V 2 ) and R-square of the prediction set ( R P 2 ) are 0.93, 0.90 and 0.91, respectively, the corresponding root mean square error of calibration set (RMSEC), root mean square error of cross-validation set (RMSECV) and root mean square error of prediction set (RMSEP) are 0.96%, 1.13%, and 1.18%, respectively. The R C 2 , R C V 2 and R P 2 of the crude protein prediction model are 0.91, 0.83 and 0.92, respectively, and the corresponding RMSEC, RMSECV and RMSEP are 0.71%, 0.97% and 0.61%, respectively. The soluble sugar prediction model has R C 2 , R C V 2 , and R P 2 of 0.64, 0.51, and 0.71, respectively, and the corresponding RMSEC, RMSECV, and RMSEP are 2.33%, 2.73%, and 2.36%, respectively. Therefore, the use of handheld NIR spectrometers combined with wavelength optimization can fastly detect the water content in fresh mulberry leaves and crude protein in dried mulberry leaves. However, it is a slightly lower predictive performance for soluble sugar in mulberry leaves.

scholarly journals Portable near Infrared Spectroscopy as a Tool for Fresh Tomato Quality Control Analysis in the Field

2021 ◽  
Vol 11 (7) ◽  
pp. 3209
Author(s):  
Karla R. Borba ◽  
Didem P. Aykas ◽  
Maria I. Milani ◽  
Luiz A. Colnago ◽  
Marcos D. Ferreira ◽  
...  
Keyword(s):  
Citric Acid ◽  
Near Infrared ◽  
Prediction Models ◽  
Quality Attributes ◽  
Internal Quality ◽  
Control Analysis ◽  
Tomato Quality ◽  
Wide Range ◽  
Different Types ◽  
Fresh Tomato

Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes non-destructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way.

A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices

2021 ◽  
Vol 175 ◽  
pp. 111497
Author(s):  
Weijie Lan ◽  
Benoit Jaillais ◽  
Catherine M.G.C. Renard ◽  
Alexandre Leca ◽  
Songchao Chen ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Apple Fruit ◽  
Internal Quality

Near infrared spectroscopy to predict internal quality of mangoes

2019 ◽  
pp. 289-294
Author(s):  
S.H.E.J. Gabriels ◽  
B. Brouwer ◽  
H. de Villiers ◽  
E. Westra ◽  
E.J. Woltering
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Internal Quality

Fast Non-Destructive Detection of Wool Content in Blended Fabrics Based on Near-Infrared Spectroscopy Technology

2015 ◽  
Vol 671 ◽  
pp. 356-362 ◽  
Author(s):  
Zhi Feng Chen ◽  
Yuan Quan Hong ◽  
Chang Jiang Wan ◽  
Lian Ying Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Detection Method ◽  
Quantitative Model ◽  
National Standard ◽  
Fast Detection ◽  
Significant Difference ◽  
Validation Set ◽  
Non Destructive

A fast non-destructive method of detection of wool content in blended fabrics was studied based on Near Infrared spectroscopy technology in order to avoid the time-consuming, tedious work and the destruction of samples in the traditional inspection. 621 wool/nylon, wool/polyester and wool/nylon/polyester blended fabrics were taken as research objects. To get the wool content, we established the wool near-infrared quantitative model by partial least squares (PLS) method after analyzing the color and composition of the samples. For verifying the validity and practicability of the model, 100 samples were chosen as an independent validation set. The variance analysis shows that there is no significant difference between Near Infrared fast detection method and national standard method (GB/T2910-2009),which indicates that this method is expected to be a means of fast non-destructive detection and will have extensive application future in the field of wool content detection.

Water absorption thermodynamics in single wood pellets modelled by multivariate near-infrared spectroscopy

Holzforschung ◽  
2008 ◽  
Vol 62 (4) ◽  
Author(s):  
Torbjörn A. Lestander
Keyword(s):  
Water Content ◽  
Near Infrared ◽  
Calorific Value ◽  
Least Squares Regression ◽  
Wood Pellets ◽  
Differential Heat ◽  
On Line ◽  
Explained Variation ◽  
Test Sets ◽  
Content Range

Abstract Samples of wood pellets were adjusted into six water content classes from 0% to 12%. The water content in single pellets varied between 0.1% and 14.2%. Three equations were constructed to estimate the differential heat of sorption (-ΔH) values from (1) fractal-geometry, (2) isosteric, and (3) calorimetric data. The ranges in calculated -ΔH of single pellets were (1) 133–1475, (2) 315–881, and (3) 195–1188 J g-1 water, respectively, across the studied moisture content range. Partial least squares regression was used to model near-infrared (NIR) spectra from single pellets and to predict -ΔH values and water content. The explained variation in test sets for the different models ranged from 97.1% to 99.9%. The shifts in peak absorbance for two water bands indicated that frequency in overtone vibration of O-H stretching and bending decreased, when water content was raised. Simulations of mixes between pellets of differential heat values showed that released heat was up to 0.03% of the gross calorific value of wood pellets. This heat may be a major contributor to initial temperature increases in pellet stacks during storage. The results indicate that on-line NIR based predictions of differential heat in wood pellets is possible to apply in the pellet industry.

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