scholarly journals Performance comparison of bench-top, hyperspectral imaging and pocket near infrared spectrometers: the example of protein quantification in wheat flour

2019 ◽  
pp. 151-155
Author(s):  
V. Baeten ◽  
J.A. Fernández Pierna ◽  
P. Vermeulen ◽  
B. Lecler ◽  
O. Minet ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Wheat Flour ◽  
Near Infrared ◽  
Performance Comparison ◽  
Protein Quantification

Quantification of protein in wheat using near infrared hyperspectral imaging: Performance comparison with conventional near infrared spectroscopy

2018 ◽  
Vol 26 (3) ◽  
pp. 186-195 ◽  
Author(s):  
Ana Morales-Sillero ◽  
Juan A. Fernández Pierna ◽  
George Sinnaeve ◽  
Pierre Dardenne ◽  
Vincent Baeten
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hyperspectral Imaging ◽  
Wavelength Range ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Performance Comparison ◽  
Imaging Instrument ◽  
Leave One Out

Hyperspectral imaging is a powerful technique that combines the advantages of near infrared spectroscopy and imaging technologies. Most hyperspectral imaging studies focus on qualitative analysis, but there is growing interest in using such technique for the quantitative analysis of agro-food products in order to use them as universal tools. The overall objective of this study was to compare the performance of a hyperspectral imaging instrument with a classical near infrared instrument for predicting chemical composition. The determination of the protein content of wheat flour was selected as example. Spectra acquisition was made in individual sealed cells using two classical near infrared instruments (NIR-DS and NIR-Perstop) and a near infrared hyperspectral line-scan camera (NIR-HSI). In the latter, they were also acquired in open cells in order to study the possibility of accelerating the measurement process. Calibration models were developed using partial least squares for the full wavelength range of each individual instrument and for the common range between instruments (1120–2424 nm). The partial least squares models were validated using the “leave-one-out” cross-validation procedure and an independent validation set. The results showed that the NIR-HSI system worked as well as the classical near infrared spectrometers when a common wavelength range was used, with an r2 of 0.99 for all instruments and Root Mean Square Error in Prediction (RMSEP) values of 0.15% for NIR-HSI and NIR-DS and 0.16% for NIR-Perstop. The high residual predictive deviation values obtained (8.08 for NIR-DS, 7.92 for NIR-HSI, and 7.56 for NIR-Perstop) demonstrate the precision of the models built. In addition, the prediction performance with open cells was almost identical to that obtained with sealed cells.

scholarly journals Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 154 ◽  
Author(s):  
Hongzhe Jiang ◽  
Fengna Cheng ◽  
Minghong Shi
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Limit Of Detection ◽  
Principal Component ◽  
Rapid Identification ◽  
Performance Comparison ◽  
Correlation Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression

Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

Subpixel detection of peanut in wheat flour using a matched subspace detector algorithm and near-infrared hyperspectral imaging

Talanta ◽  
2020 ◽  
Vol 216 ◽  
pp. 120993 ◽  
Author(s):  
Antoine Laborde ◽  
Benoît Jaillais ◽  
Jean-Michel Roger ◽  
Maxime Metz ◽  
Delphine Jouan-Rimbaud Bouveresse ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Wheat Flour ◽  
Near Infrared

scholarly journals Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour

2018 ◽  
Vol 8 (7) ◽  
pp. 1076 ◽  
Author(s):  
Xin Zhao ◽  
Wei Wang ◽  
Xinzhi Ni ◽  
Xuan Chu ◽  
Yu-Feng Li ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Wheat Flour ◽  
Near Infrared ◽  
Whole Wheat Flour ◽  
Whole Wheat

Utilising near-infrared hyperspectral imaging to detect low-level peanut powder contamination of whole wheat flour

2019 ◽  
Vol 184 ◽  
pp. 55-68 ◽  
Author(s):  
Xin Zhao ◽  
Wei Wang ◽  
Xinzhi Ni ◽  
Xuan Chu ◽  
Yu-Feng Li ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Wheat Flour ◽  
Near Infrared ◽  
Whole Wheat Flour ◽  
Low Level ◽  
Whole Wheat

Pork Tissue Recognition Using Visible and Near Infrared Hyperspectral Imaging

2020 ◽  
Author(s):  
Chih-Cheng Pai ◽  
Yang-Chu Chen ◽  
Keng-Hao Liu ◽  
Yuan-Hsun Tsai ◽  
Po-Chi Hu ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared

scholarly journals Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires

2020 ◽  
Author(s):  
L. Granlund ◽  
M. Keinänen ◽  
T. Tahvanainen
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Support Vector ◽  
High Resolution Imaging ◽  
Factors Affecting ◽  
Vector Machines ◽  
Resolution Imaging ◽  
Shortwave Infrared ◽  
Type Classification ◽  
Wide Potential

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.

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