scholarly journals A study of starch content detection and the visualization of fresh-cut potato based on hyperspectral imaging

RSC Advances ◽  
2021 ◽  
Vol 11 (22) ◽  
pp. 13636-13643
Author(s):  
Fuxiang Wang ◽  
Chunguang Wang ◽  
Shiyong Song
Keyword(s):  
Hyperspectral Imaging ◽  
Starch Content ◽  
Potato Chips ◽  
Fresh Cut

The starch content of fresh-cut potato chips was detected via hyperspectral imaging, and a representation of the visual distribution of the starch content was realized.

Detection of browning of fresh‐cut potato chips based on machine vision and electronic nose

2021 ◽  
Author(s):  
Tu Hongyang ◽  
Huang Daming ◽  
Huang Xingyi ◽  
Joshua Harrington Aheto ◽  
Ren Yi ◽  
...  
Keyword(s):  
Machine Vision ◽  
Electronic Nose ◽  
Potato Chips ◽  
Fresh Cut

Determination of starch content in adulterated fresh cheese using hyperspectral imaging

2018 ◽  
Vol 21 ◽  
pp. 14-19 ◽  
Author(s):  
Abel Barreto ◽  
J.P. Cruz-Tirado ◽  
Raúl Siche ◽  
Roberto Quevedo
Keyword(s):  
Hyperspectral Imaging ◽  
Starch Content ◽  
Fresh Cheese

Fluorescence hyperspectral imaging technique for foreign substance detection on fresh-cut lettuce

2017 ◽  
Vol 97 (12) ◽  
pp. 3985-3993 ◽  
Author(s):  
Changyeun Mo ◽  
Giyoung Kim ◽  
Moon S Kim ◽  
Jongguk Lim ◽  
Hyunjeong Cho ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Technique ◽  
Foreign Substance ◽  
Fresh Cut

scholarly journals Study on starch content detection and visualization of potato based on hyperspectral imaging

2021 ◽  
Author(s):  
Fuxiang Wang ◽  
Chunguang Wang ◽  
Shiyong Song ◽  
Shengshi Xie ◽  
Feilong Kang
Keyword(s):  
Hyperspectral Imaging ◽  
Starch Content

scholarly journals Comparison of the Non-Invasive Monitoring of Fresh-Cut Lettuce Condition with Imaging Reflectance Hyperspectrometer and Imaging PAM-Fluorimeter

Photonics ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 425
Author(s):  
Boris Shurygin ◽  
Olga Chivkunova ◽  
Olga Solovchenko ◽  
Alexei Solovchenko ◽  
Alexey Dorokhov ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Techniques ◽  
Pulse Amplitude ◽  
Hyperspectral Data ◽  
Invasive Monitoring ◽  
Leaf Quality ◽  
Non Invasive ◽  
Quality Degradation ◽  
Advanced Stages ◽  
Fresh Cut

We compared two approaches to non-invasive proximal sensing of the early changes in fresh-cut lettuce leaf quality: hyperspectral imaging and imaging of variable chlorophyll fluorescence contained in the leaves. The estimations made by the imaging techniques were confronted with the quality assessments made by traditional biochemical assays (i.e., relative water content and foliar pigment (chlorophyll and carotenoid) composition. The hyperspectral imaging-based approach provided the highest sensitivity to the decline of fresh-cut lettuce leaf quality taking place within 24 h from cutting. Using of the imaging pulse-amplitude modulated PAM chlorophyll fluorometer was complicated by (i) weak correlation of the spatial distribution pattern of the Qy parameter with the actual physiological condition of the plant object and (ii) its high degree of heterogeneity. Accordingly, the imaging PAM-based approach was sensitive only to the manifestations of leaf quality degradation at advanced stages of the process. Sealing the leaves in polyethylene bags slowed down the leaf quality degradation at the initial stages (<three days) but promoted its rate at more advanced stages, likely due to build-up of ethylene in the bags. An approach was developed to the processing of hyperspectral data for non-invasive monitoring of the lettuce leaves with a potential for implementation in greenhouses and packing lines.

scholarly journals Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer

Molecules ◽  
2020 ◽  
Vol 25 (7) ◽  
pp. 1651 ◽  
Author(s):  
Xiulin Bai ◽  
Qinlin Xiao ◽  
Lei Zhou ◽  
Yu Tang ◽  
Yong He
Keyword(s):  
Sulfur Dioxide ◽  
Hyperspectral Imaging ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Support Vector ◽  
Svm Model ◽  
Fresh Cut ◽  
Potato Slices

Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky–Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.

On-line fresh-cut lettuce quality measurement system using hyperspectral imaging

2017 ◽  
Vol 156 ◽  
pp. 38-50 ◽  
Author(s):  
Changyeun Mo ◽  
Giyoung Kim ◽  
Moon S. Kim ◽  
Jongguk Lim ◽  
Kangjin Lee ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Measurement System ◽  
Quality Measurement ◽  
On Line ◽  
Fresh Cut

Development of simplified models for the nondestructive testing of rice with husk starch content using hyperspectral imaging technology

2019 ◽  
Vol 11 (46) ◽  
pp. 5910-5918 ◽  
Author(s):  
Zhehao Zhang ◽  
Xiang Yin ◽  
Chengye Ma
Keyword(s):  
Nondestructive Testing ◽  
Hyperspectral Imaging ◽  
Predictive Models ◽  
Imaging System ◽  
Starch Content ◽  
Simplified Models ◽  
Rice Varieties ◽  
Imaging Technology

In this study, we aimed to establish the predictive models of the starch content in rice (with husk) using a hyperspectral imaging system (HSI) for a collection of 87 different rice varieties in China.

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