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.

Identification of Rice Variety Using near Infrared Spectroscopy

1998 ◽  
Vol 6 (A) ◽  
pp. A67-A73 ◽  
Author(s):  
Y.K. Kwon ◽  
R.K. Cho
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rice Variety ◽  
Principal Component ◽  
Dna Fingerprint ◽  
Image Processing Technique ◽  
Rice Varieties ◽  
The Government ◽  
Non Destructive

Current techniques for the discrimination of varieties of rice is known not to be objective because of having to depend on the naked eye of a well trained inspector. DNA fingerprint methods, based on the genetic character of rice, has been shown to be inappropriate for on-site application because the method needs a lot of labour and skilled expertise. In order to solve these problems, many researchers have tried to develop a non-destructive method which possesses speed and functionality for the inspection centre of the government. Rice consumers want tot confirm cultivation origin and its variety because they believe price or eating score has a high correlation according to them. Sometimes illegal utilisation of rice has been reported and the variety name has been mismatched with the actual rice variety due to the non-establishment of objective techniques for discrimination of rice origin and variety. On the basis of this situation, we investigated the possibility of developing a non-destructive discriminant method for the domestic rice variety in this research. At the 8th IDRC held at Chambersburg in 1996 we reported that an image processing technique using a CCD camera could discriminate the variety of rice with high accuracy in the case of quite different shaped rice but the accuracy reached 90% for similarly shaped rice. At the prsent conference, near infrared spectroscopy, which can offer internal characters of a single rice kernel, giving useful information for identifying rice variety, was introduced. We have developed several tools for measuing the NIR spectra of whole, polished rice. The spectra using a single grain cell with grinding facilities was more effective than the others in accuracy of identification after principal component analysis. In conclusion, near infrared spectroscopy can be used to identify rice varieties even though even though accuracy of identification needs to be improved.

scholarly journals Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

2021 ◽  
Author(s):  
Pedro N.S. Sampaio ◽  
Carla Brites
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Biochemical Properties ◽  
Agricultural Materials ◽  
Non Destructive

Nowadays, the conventional biochemical methods used to differentiate and characterize rice types, biochemical properties, authentication, and contamination issues are difficult to implement due to the high cost of reagents, time requirement and environmental issues. Actually, the success of agri-food technology is directly related to the quality of analysis of experimental data acquired by sensors or techniques such as the infrared-spectroscopy. To overcome these technical limitations, a rapid and non-destructive methodology for discrimination and classification of rice has been investigated. Near-infrared spectroscopy is considered as fast, clean, and non-destructive analytical tools and its spectra present significant biomolecular information that must be analysed by sophisticated methodologies. Machine learning plays an important role in the analysis of the spectral data being used several methods such as Partial Least Squares, Principal Component Analysis, Partial Least Squares-Discriminant Analysis, Support Vector Machine, Artificial Neuronal Network, among others which can successfully be applied for food classification and discrimination as well as in terms of authentication and contamination issues. The quality control of rice is extremely important at every stage of production, beginning with estimation of raw agricultural materials and monitoring their quality during storage, estimating food quality during the production process and of the final products as well as the determination of their authenticity and the detection of adulterants.

scholarly journals Evaluation of sapodilla fruit quality using near-infrared spectroscopy

2018 ◽  
Author(s):  
Yuda Hadiwijaya
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Fruit Quality ◽  
Near Infrared ◽  
Prediction Models ◽  
Partial Least Square ◽  
Least Square ◽  
Plant Production ◽  
Infrared Spectrometer ◽  
Non Destructive

Fruit quality detection using near-infrared spectroscopy is a fast, accurate, and non-destructive method. Hence the fruits can still be marketed after the measurement. The purpose of this study was to analyze the quality of sapodilla fruit using near-infrared spectroscopy. The study was conducted in March to August 2017 at the Plant Production Technology Laboratory of Horticulture Division, Agriculture Faculty of Padjadjaran University, Jatinangor. The method used in this study was multivariate data analysis of chemometrics. The spectra data were obtained using portable nearinfrared spectrometer (NirVana AG410, Integrated Spectronics Pty, Ltd, Australia) with wavelength range of 312-1050 nm. Calibration and prediction models were acquired using partial least square (PLS). The results revealed that non-destructive method using near-infrared spectrometer was able to measure sapodilla fruit quality such as, firmness, total dissolved solids, and color values including L*, a*, b*, ho, and C*.

scholarly journals Rapid quantification of rice (Oryza sativa) qualities based on adaptive near infrared spectroscopy

2021 ◽  
Vol 922 (1) ◽  
pp. 012020
Author(s):  
R Hayati ◽  
A A Munawar ◽  
A Marliah
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component ◽  
Component Analysis ◽  
Partial Least Square ◽  
Rice Samples ◽  
Rapid Quantification

Abstract Determination of rice quality parameters is the key factor affecting sustainable agriculture practices. The main purpose of this present study is to develop prediction models based on adaptive near infrared spectroscopy (NIRS) for rapid quantification of rice qualities in form of protein content. Rice samples were obtained from several paddy field in Aceh province with different cultivars. Near infrared spectral data of rice samples were acquired and in wavelength range from 1000 to 2500 nm and recorded as diffuse reflectance spectrum. Prediction models were established using principal component analysis (PCA), principal component analysis (PCR) and partial least square regression (PLSR). The results showed that NIRS combined with PCA can classify rice samples based on their cultivars. Moreover, this approach with PCR and PLSR can also predicted and determined protein contents with satisfactory performance achieving maximum correlation coefficient (r) of 0.81 and ratio prediction to deviation (RPD) index of 2.84 for PCR and r of 0.90 and RPD of 3.19 for PLSR respectively. Based on achieved results, it may conclude that adaptive NIRS approach can be used to quantify rice qualities rapidly and non-destructively.

Study on the Vigour Testing of Soybean Seed Based on Near Infrared Spectroscopy Technology

2011 ◽  
Vol 58-60 ◽  
pp. 458-462 ◽  
Author(s):  
Xing Wei Qi ◽  
Wei Kai Li ◽  
Wei Li ◽  
He Li
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Soybean Seed ◽  
Principal Component ◽  
Seed Vigor ◽  
Seed Vigour ◽  
Pattern Recognition Methods ◽  
Distance Model ◽  
Non Destructive

In the experiment, a new non-destructive method for discriminating three vigour of soybean seeds was developed by near infrared spectroscopy (NIRS) combined with principal component and Mahalanobis distance model. The result show that NORMALIZE was the best pretreatment method of NORMALIZE, MSC, First Derivative and Second Derivative, the ideal spectral acquisition range of Soybean seed was 4000cm-1～6900cm-1, the cumulative contribution rate of the front four principal components reached 99.82%, it established the best model. The model could successfully complete the calibration samples and identify the prediction sample. This study proved that the use of near infrared spectroscopy combined with pattern recognition methods to rapid and nondestructive determination of seed vigor was indeed feasible. This study will offer a new method of testing soybean seed vigour with quick, non-destructive characters.

scholarly journals Research Note: Non-destructive Detection of Super Grade Chick Embryos or Hatchlings using Near-infrared Spectroscopy

2021 ◽  
pp. 101189
Author(s):  
Alin Khaliduzzaman ◽  
Ayuko Kashimori ◽  
Tetsuhito Suzuki ◽  
Yuichi Ogawa ◽  
Naoshi Kondo
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Research Note ◽  
Chick Embryos ◽  
Non Destructive
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