scholarly journals Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology

2020 ◽  
Vol 10 (16) ◽  
pp. 5399
Author(s):  
Xiaopeng Sun ◽  
Sai Xu ◽  
Huazhong Lu
Keyword(s):  
Machine Vision ◽  
Near Infrared ◽  
Citrus Fruit ◽  
Classification Model ◽  
Internal Quality ◽  
Soluble Solid Content ◽  
Infrared Transmittance ◽  
On Line ◽  
Non Destructive ◽  
Juice Sacs

Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400–1100 nm and 900–1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.

Proper Light Array Design for the Optimum Nondestructive Evaluation of Fruit’s Internal Quality

2006 ◽  
Vol 321-323 ◽  
pp. 1201-1204
Author(s):  
Suk Won Kang ◽  
Kang Jin Lee ◽  
Jae Ryong Son
Keyword(s):  
Light Source ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Near Infrared ◽  
Detection System ◽  
Transmittance Spectrum ◽  
Internal Quality ◽  
Light Power ◽  
Infrared Transmittance ◽  
Non Destructive

This study was conducted to develop an accurate quality evaluation system based on optimized factors such as light source array and light power, which are used in non-destructive fruit sorter to obtain the internal quality information of fruits using the near infrared transmittance spectra. It is necessary to provide the proper design guide for the light source part in the existing non-destructive fruit sorters for apples and pears, and to measure the real-time near infrared transmittance spectrum without the leakage of light. The near infrared transmittance spectrum detection system was developed with the light source part which has the power-controllable 12 halogen lamps (100W/12V) with gold coating, light detection part, and transfer line. By using the accurate control of the voltage and current (maximum power is 1.2kW) in light power control part, it is concluded that the minimum power for the internal quality evaluation of apples and pears was over 0.5 kW. To prevent the leakage of light, the array of light source was rearranged and tested. Without changing the tray structure, it is concluded that the leakage of light can be prevented by the proper array of light source and power. For the irradiation for the moving apples and pears, 2 upper lamps and 4 lower lamps combination did not have leakage of light and the correlation coefficient of this combination shows the 0.90 for apples and 0.96 for pears.

On-line non-destructive determination of proteins and infiltrated fat in Iberian pork loin by near infrared spectrometry with a remote reflectance fibre optic probe

2002 ◽  
Vol 453 (2) ◽  
pp. 281-288 ◽  
Author(s):  
Inmaculada González-Martı́n ◽  
Claudio González-Pérez ◽  
Jesús Hernández-Méndez ◽  
Noelia Alvarez-Garcı́a ◽  
José-Luis Hernández Andaluz
Keyword(s):  
Near Infrared ◽  
Infrared Spectrometry ◽  
Fibre Optic ◽  
Fibre Optic Probe ◽  
On Line ◽  
Near Infrared Spectrometry ◽  
Pork Loin ◽  
Optic Probe ◽  
Non Destructive

scholarly journals NON-DESTRUCTIVE QUANTIFICATION OF CAROTENOIDS IN INTACT WATERMELON (Citrullus lanatus) USING ON-LINE NEAR INFRARED SPECTROSCOPY

2015 ◽  
Author(s):  
Elena Tamburini ◽  
Giuseppe Ferrari ◽  
Paola Pedrini ◽  
Valentina Donegà ◽  
Marco Malavasi ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Citrullus Lanatus ◽  
On Line ◽  
Non Destructive

scholarly journals Near-Infrared Transmittance Spectral Imaging for Nondestructive Measurement of Internal Disorder in Korean Ginseng

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 273 ◽  
Author(s):  
Lalit Mohan Kandpal ◽  
Jayoung Lee ◽  
Hyungjin Bae ◽  
Moon S. Kim ◽  
Insuck Baek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Imaging System ◽  
Quality Measurement ◽  
Spectral Imaging ◽  
Cost Effective ◽  
Ginseng Root ◽  
Internal Quality ◽  
Infrared Transmittance ◽  
Significant Difference ◽  
Novel Strategy

The grading of ginseng (Panax ginseng) including the evaluation of internal quality attributes is essential in the ginseng industry for quality control. Assessment for inner whitening, a major internal disorder, must be conducted when identifying high quality ginseng. Conventional methods for detecting inner whitening in ginseng root samples use manual inspection, which is time-consuming and inaccurate. This study develops an internal quality measurement technique using near-infrared transmittance spectral imaging to evaluate inner whitening in ginseng samples. Principle component analysis (PCA) was used on ginseng hypercube data to evaluate the developed technique. The transmittance spectra and spectral images of ginseng samples exhibiting inner whitening showed weak intensity characteristics compared to normal ginseng in the region of 900–1050 nm and 1150–1400 nm respectively, owing to the presence of whitish internal tissues that have higher optical density. On the basis of the multivariate analysis method, even a simple waveband ratio image has the great potential to quickly detect inner whitening in ginseng samples, since these ratio images show a significant difference between whitened and non-whitened regions. Therefore, it is possible to develop an efficient and rapid spectral imaging system for the real-time detection of inner whitening in ginseng using minimal spectral wavebands. This novel strategy for the rapid, cost-effective, non-destructive detection of ginseng’s inner quality can be a key component for the automation of ginseng grading.

Monitoring Carotenoid and Chlorophyll Pigments in Virgin Olive Oil by Visible-Near Infrared Transmittance Spectroscopy. On-Line Application

2003 ◽  
Vol 11 (3) ◽  
pp. 219-226 ◽  
Author(s):  
A. Jiménez Márquez
Keyword(s):  
Olive Oil ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Virgin Olive Oil ◽  
Reference Laboratory ◽  
Least Squares Regression ◽  
Chlorophyll Pigments ◽  
Infrared Transmittance ◽  
On Line

Visible-near infrared transmittance spectroscopy was used to determine total levels of chlorophyll and carotenoid in virgin olive oil. Calibration models were developed in the laboratory using partial least squares regression. An initial smoothing followed by a first derivative treatment was the best signal correction. The validation set gave a correlation coefficient and standard error of prediction of 0.985 and 0.66 mg kg−1 for carotene totals and 0.993 and 0.96 mg kg−1 for chlorophyll totals. These partial least squares models were used to monitor on-line levels of these compounds during virgin olive oil processing in olive oil mills. The results indicate similarity between both visible-near infrared transmittance spectroscopy and reference laboratory methods.

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