scholarly journals Apple Internal Quality Inspection Using Hyperspectral Image Technology

2017 ◽  
Author(s):  
Xiao-Yan Chen ◽  
Wen-Tao Chen ◽  
Jia-Sui Lv ◽  
Xiang Long ◽  
Tao Pang
Keyword(s):  
Hyperspectral Image ◽  
Internal Quality ◽  
Quality Inspection

scholarly journals Buying Wisely

2008 ◽  
Vol 130 (10) ◽  
pp. 39-41
Author(s):  
Stephen Gree
Keyword(s):  
Quality Control ◽  
Competitive Bidding ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Engineering Practice ◽  
Quality Inspection ◽  
Measurement Devices ◽  
Sound Engineering ◽  
Specification Document ◽  
Factory Site

This article reviews about rigorous equipment specification, which is a sound engineering practice and is important in capital procurement. A complete and robust specification document serves as the basis of all important procurement activities: requesting a bid and competitive bidding, purchasing contract development, and interim and final quality inspection of the delivered equipment. Once the bids have been received, a rigorous, organized, and documented bid analysis should be done. A format that incorporates a comparison of critical variables’ values in each bid should be designed. A rigorous post-delivery inspection is required because there may be serious flaws with the equipment that escaped the manufacturer’s internal quality control. The engineer is better able to thoroughly inspect the equipment at the company site rather than the factory site. Checks involving measurement devices should be included, depending on the type of equipment ordered. Since this is usually precommissioning activity, calibration of instrumentation for this activity is desired, though not required.

scholarly journals Nondestructive Internal Quality Inspection of Tomato Fruits

10.5109/4512 ◽  
2003 ◽  
Vol 47 (2) ◽  
pp. 419-426
Author(s):  
Wenzhong Hu ◽  
Toshitaka Uchino ◽  
Daisuke Hamanaka ◽  
Hussain Sorour ◽  
Yoshiaki Hori ◽  
...  
Keyword(s):  
Internal Quality ◽  
Quality Inspection ◽  
Tomato Fruits

scholarly journals Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhenzhu Su ◽  
Chu Zhang ◽  
Tianying Yan ◽  
Jianan Zhu ◽  
Yulan Zeng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Soluble Solids ◽  
Quality Inspection ◽  
Soluble Solids Content ◽  
Saliency Maps ◽  
Solids Content ◽  
Maturity Degree

Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

scholarly journals Predicting of the Quality Attributes of Orange Fruit Using Hyperspec-tral Images

2019 ◽  
Author(s):  
V. Aredo ◽  
L. Velásquez ◽  
J. Carranza-Cabrera ◽  
R. Siche
Keyword(s):  
Hyperspectral Image ◽  
Quality Attributes ◽  
Soluble Solids ◽  
Coefficient Of Determination ◽  
Internal Quality ◽  
Least Squares Regression ◽  
Hyperspectral Reflectance ◽  
Simplified Models ◽  
External Quality ◽  
Orange Fruit

Background: Hyperspectral image analysis is a fast and non-destructive technique that is being used to measure quality attributes of food products. This research investigated the feasibility of predicting internal quality attributes, such as Total Soluble Solids (TSS), pH, Titratable Acidity (TA), and maturity index (TSS/TA); and external quality attributes such as color components (L*, a*, b*) as well as Color Index (CI) of Valencia orange fruit using hyperspectral reflectance imaging in the range of 400-1000 nm. Methods: Oranges were scanned by the system in order to build full models for predicting quality attributes using partial least squares regression. Optimal wavelengths were identified using the regression coefficients from full models, which were used to build simplified models by multiple linear regression. The coefficient of determination of prediction (R2p) and the Standard Error of Prediction (SEP) were used to measure the performance of the models obtained. Results: Full models for internal quality attributes had low performance (R2p<0.3, SEP>50%). Full models for external quality attributes presented a high performance for L* (R2p=0.898, SEP=19%), a* (R2p=0.952, SEP=13%), b* (R2p=0.922, SEP=20%), and CI (R2p=0.972, SEP=12%). The simplified models presented similar performance to those obtained for external quality attributes. Conclusion: Hyperspectral reflectance imaging has potential for predicting color of oranges in an objective and noncontact way.

scholarly journals Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning

Food Control ◽  
2020 ◽  
Vol 113 ◽  
pp. 107170 ◽  
Author(s):  
Tim Van De Looverbosch ◽  
Md. Hafizur Rahman Bhuiyan ◽  
Pieter Verboven ◽  
Manuel Dierick ◽  
Denis Van Loo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internal Quality ◽  
Quality Inspection ◽  
Pear Fruit ◽  
X Ray

scholarly journals Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5021
Author(s):  
Baohua Yang ◽  
Yuan Gao ◽  
Qian Yan ◽  
Lin Qi ◽  
Yue Zhu ◽  
...  
Keyword(s):  
Fruit Quality ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Solid Content ◽  
Internal Quality ◽  
Accurate Estimation ◽  
Soluble Solid Content ◽  
Model Based ◽  
Soluble Solid ◽  
Non Destructive

Soluble solids content (SSC) is one of the important components for evaluating fruit quality. The rapid development of hyperspectral imagery provides an efficient method for non-destructive detection of SSC. Previous studies have shown that the internal quality evaluation of fruits based on spectral information features achieves better results. However, the lack of comprehensive features limits the accurate estimation of fruit quality. Therefore, the deep learning theory is applied to the estimation of the soluble solid content of peaches, a method for estimating the SSC of fresh peaches based on the deep features of the hyperspectral image fusion information is proposed, and the estimation models of different neural network structures are designed based on the stack autoencoder–random forest (SAE-RF). The results show that the accuracy of the model based on the deep features of the fusion information of hyperspectral imagery is higher than that of the model based on spectral features or image features alone. In addition, the SAE-RF model based on the 1237-650-310-130 network structure has the best prediction effect (R2 = 0.9184, RMSE = 0.6693). Our research shows that the proposed method can improve the estimation accuracy of the soluble solid content of fresh peaches, which provides a theoretical basis for the non-destructive detection of other components of fresh peaches.

The Washington Consensus, liberal ideas and terms of the military-technological policy

2020 ◽  
Vol 16 (11) ◽  
pp. 2103-2123
Author(s):  
V.L. Gladyshevskii ◽  
E.V. Gorgola ◽  
D.V. Khudyakov
Keyword(s):  
United States ◽  
Production Management ◽  
Developed Countries ◽  
The United States ◽  
Internal Quality ◽  
Industrial Complex ◽  
Active Involvement ◽  
Military Industrial Complex ◽  
Industrial Complexes ◽  
The Military

Subject. In the twentieth century, the most developed countries formed a permanent military economy represented by military-industrial complexes, which began to perform almost a system-forming role in national economies, acting as the basis for ensuring national security, and being an independent military and political force. The United States is pursuing a pronounced militaristic policy, has almost begun to unleash a new "cold war" against Russia and to unwind the arms race, on the one hand, trying to exhaust the enemy's economy, on the other hand, to reindustrialize its own economy, relying on the military-industrial complex. Objectives. We examine the evolution, main features and operational distinctions of the military-industrial complex of the United States and that of the Russian Federation, revealing sources of their military-technological and military-economic advancement in comparison with other countries. Methods. The study uses military-economic analysis, scientific and methodological apparatus of modern institutionalism. Results. Regulating the national economy and constant monitoring of budget financing contribute to the rise of military production, especially in the context of austerity and crisis phenomena, which, in particular, justifies the irrelevance of institutionalists' conclusions about increasing transaction costs and intensifying centralization in the industrial production management with respect to to the military-industrial complex. Conclusions. Proving to be much more efficient, the domestic military-industrial complex, without having such access to finance as the U.S. military monopolies, should certainly evolve and progress, strengthening the coordination, manageability, planning, maximum cost reduction, increasing labor productivity, and implementing an internal quality system with the active involvement of the State and its resources.

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