Numerical study of light propagation in agricultural products for non-destructive assessment of food quality

2015 ◽  
Author(s):  
Kiyohito Hattori ◽  
Hiroyuki Fujii ◽  
Yuki Tatekura ◽  
Kazumichi Kobayashi ◽  
Masao Watanabe
Keyword(s):  
Food Quality ◽  
Light Propagation ◽  
Numerical Study ◽  
Agricultural Products ◽  
Non Destructive

scholarly journals Quality Inspection of Food and Agricultural Products using Artificial Intelligence

2021 ◽  
Author(s):  
Maimunah Mohd Ali ◽  
Norhashila Hashim ◽  
Samsuzana Abd Aziz ◽  
Ola Lasekan
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Food Quality ◽  
High Performance ◽  
Agricultural Products ◽  
Special Focus ◽  
Quality Inspection ◽  
Big Data Technologies ◽  
Artificial Neural Network Ann ◽  
Non Destructive

A rising awareness for quality inspection of food and agricultural products has generated a growing effort to develop rapid and non-destructive techniques. Quality detection of food and agricultural products has prime importance in various stages of processing due to the laborious processes and the inability of the system to measure the whole of the food production. The detection of food quality has previously depended on various destructive techniques that require sample destruction and a large amount of postharvest losses. Artificial Intelligence (AI) has emerged with big data technologies and high-performance computation to create new opportunities in the multidisciplinary agri-food domain. This review presents the key concepts of AI comprising an expert system, artificial neural network (ANN), and fuzzy logic. A special focus is laid on the strength of AI applications in determining food quality for producing high and optimum yields. It was demonstrated that ANN provides the best result for modelling and effective in real-time monitoring techniques. The future use of AI for assessing quality inspection is promising which could lead to a real-time as well as rapid evaluation of various food and agricultural products.

scholarly journals AlexNet convolutional neural network to classify the types of Indonesian coffee beans

2021 ◽  
Vol 905 (1) ◽  
pp. 012059
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
F I Ilmi ◽  
M R Fauzy ◽  
R Damayanti ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Sensitivity Analysis ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Agricultural Products ◽  
Coffee Bean ◽  
Coffee Beans ◽  
Non Destructive

Abstract Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans, i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp and the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.

scholarly journals Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

Foods ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 927 ◽  
Author(s):  
Akinbode A. Adedeji ◽  
Nader Ekramirad ◽  
Ahmed Rady ◽  
Ali Hamidisepehr ◽  
Kevin D. Donohue ◽  
...  
Keyword(s):  
Food Quality ◽  
Fruits And Vegetables ◽  
New Technologies ◽  
Fresh Produce ◽  
Detection Methods ◽  
Future Research ◽  
Insect Infestation ◽  
Noninvasive Methods ◽  
Synthetic Chemicals ◽  
Non Destructive

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers’ expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects’ attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import–export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods’ application in the detection and classification of insect infestation in fruits and vegetables.

Time-resolved reflectance spectroscopy for non-destructive assessment of food quality

2008 ◽  
Vol 2 (2) ◽  
pp. 82-89 ◽  
Author(s):  
Alessandro Torricelli ◽  
Lorenzo Spinelli ◽  
Davide Contini ◽  
Maristella Vanoli ◽  
Anna Rizzolo ◽  
...  
Keyword(s):  
Food Quality ◽  
Reflectance Spectroscopy ◽  
Time Resolved ◽  
Non Destructive

scholarly journals Agricultural products quality determination by means of near infrared spectroscopy

2022 ◽  
Vol 951 (1) ◽  
pp. 012112
Author(s):  
A A Munawar ◽  
Z Zulfahrizal ◽  
R Hayati ◽  
Syahrul
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Agricultural Products ◽  
Cocoa Bean ◽  
Cocoa Beans ◽  
Non Destructive

Abstract Cocoa is one of main agricultural products cultivated in many tropical countries and processed onto several derivative products. To determine cocoa beans qualities, laboratory procedures based on solvent extractions were mainly used, however most of them are destructive and may cause environmental pollutions. The main purpose of this present study is to employ near infrared spectroscopy (NIRS) for rapid and non-destructive assessment of cocoa beans in form of fat content. Near infrared spectral data of cocoa bean samples were measured as diffuse reflectance in wavelength range from 1000 to 2500 nm. Reference fat contents were measured using standard laboratory methods. Prediction models were developed using principal component regression with raw and baseline corrected spectra data. The results showed that fat contents of cocoa beans can be predicted and determined with maximum correlation coefficient (r) of 0.89 and ratio prediction to deviation (RPD) index of 2.87 for raw spectra and r of 0.91, RPD of 3.18 for baseline spectra correction. It may conclude that NIRS was feasible to be applied as a rapid and non-destructive method for cocoa bean quality assessment.

Biospeckle laser technique – A novel non-destructive approach for food quality and safety detection

2020 ◽  
Vol 97 ◽  
pp. 1-13 ◽  
Author(s):  
R. Pandiselvam ◽  
V.P. Mayookha ◽  
Anjineyulu Kothakota ◽  
S.V. Ramesh ◽  
Rohit Thirumdas ◽  
...  
Keyword(s):  
Food Quality ◽  
Quality And Safety ◽  
Laser Technique ◽  
Food Quality And Safety ◽  
Non Destructive

Applications of Visible and near-Infrared Hyperspectral Imaging for Non-Destructive Detection of the Agricultural Products

2011 ◽  
Vol 317-319 ◽  
pp. 909-914
Author(s):  
Ying Lan Jiang ◽  
Ruo Yu Zhang ◽  
Jie Yu ◽  
Wan Chao Hu ◽  
Zhang Tao Yin
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Imaging Technique ◽  
Agricultural Products ◽  
Quality Inspection ◽  
Hyperspectral Reflectance ◽  
Imaging And Spectroscopy ◽  
Non Destructive

Agricultural products quality which included intrinsic attribute and extrinsic characteristic, closely related to the health of consumer and the exported cost. Now, imaging (machine vision) and spectrum are two main nondestructive inspection technologies to be applied. Hyperspectral imaging, a new emerging technology developed for detecting quality of the food and agricultural products in recent years, combined techniques of conventional imaging and spectroscopy to obtain both spatial and spectral information from an objective simultaneously. This paper compared the advantage and disadvantage of imaging, spectrum and hyperspectral imaging technique, and provided a description to basic principle, feature of hyperspectral imaging system and calibration of hyperspectral reflectance images. In addition, the recent advances for the application of hyperspectral imaging to agricultural products quality inspection were reviewed in other countries and China.

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