Measurement of moisture content for rough rice by visible and near-infrared (NIR) spectroscopy

Heman, Adcha; Hsieh, Chia‐Wei

doi:10.1016/j.eaef.2016.02.002

Cited by 24 publications

(9 citation statements)

References 10 publications

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“…Heman et al. established a model to determine the moisture content of rice and obtained an R 2 value of .920 in external validation of the model (Heman & Hsieh, ). Fassio et al.…”

Section: Resultsmentioning

confidence: 99%

Determination of soybean routine quality parameters using near‐infrared spectroscopy

ZhenYing

Chen

et al. 2018

Food Science & Nutrition

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Large differences in quality existed between soybean samples. In order to rapidly detect soybean quality between samples from different areas, we have developed near‐infrared spectroscopy (NIRS) models for the moisture, crude fat, and protein content of soybeans, based on 360 soybean samples collected from different areas. Compared with whole kernels, soybean powder with particle sizes of 60 mesh was more suitable for modeling of moisture, crude fat, and protein content. To increase the reproducibility of the prediction model, uniform particle sizes of soybeans were prepared by grinding and sieving soybeans with different sizes and colors. Modeling analysis showed that the internal cross‐validation correlation coefficients (R cv) for the moisture, crude fat, and protein content of soybeans were .965, .941, and .949, respectively, and the determination coefficients (R 2) were .966, .958, and .958. NIRS performed well as a rapid method for the determination of routine quality parameters and provided reference data for the analysis of soybean quality using FT‐NIRS.

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“…Heman et al. established a model to determine the moisture content of rice and obtained an R 2 value of .920 in external validation of the model (Heman & Hsieh, ). Fassio et al.…”

Section: Resultsmentioning

confidence: 99%

Determination of soybean routine quality parameters using near‐infrared spectroscopy

ZhenYing

Chen

et al. 2018

Food Science & Nutrition

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“…Although near-infrared spectra (NIR) analysis depends on the quality of model established by species, growth stage, available spectral range and some other factors [26], the main advantages of the NIR methods are low costs, quick and accurate responses, nondestructive analysis, and no need for or minimum sample preparation or manipulation with hazardous chemicals/ solvents [27]. Previously, rice crop VNIR has been utilized for the detection of chlorophyll, nitrogen content, moisture content, starch quality, protein activity and amino acid content [28][29][30][31][32]. Wu et al [33] and Sánchez et al [34] used NIR technology to determine herbicide or pesticide levels in vegetables and food.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging

et al. 2020

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Background: To investigate potential effects of herbicide phytotoxic on crops, a major challenge is a lack of nondestructive and rapid methods to detect plant growth that could allow characterization of herbicide-resistant plants.In such a case, hyperspectral imaging can quickly obtain the spectrum for each pixel in the image and monitor status of plants harmlessly. Method:Hyperspectral imaging covering the spectral range of 380-1030 nm was investigated to determine the herbicide toxicity in rice cultivars. Two rice cultivars, Xiushui 134 and Zhejing 88, were respectively treated with quinclorac alone and plus salicylic acid (SA) pre-treatment. After ten days of treatments, we collected hyperspectral images and physiological parameters to analyze the differences. The score images obtained were used to explore the differences among samples under diverse treatments by conducting principal component analysis on hyperspectral images. To get useful information from original data, feature extraction was also conducted by principal component analysis. In order to classify samples under diverse treatments, full-spectra-based support vector classification (SVC) models and extracted-feature-based SVC models were established. The prediction maps of samples under different treatments were constructed by applying the SVC models using extracted features on hyperspectral images, which provided direct visual information of rice growth status under herbicide stress. The physiological analysis with the changes of stress-responsive enzymes confirmed the differences of samples under different treatments. Results:The physiological analysis showed that SA alleviated the quinclorac toxicity by stimulating enzymatic activity and reducing the levels of reactive oxygen species. The score images indicated there were spectral differences among the samples under different treatments. Full-spectra-based SVC models and extracted-feature-based SVC models obtained good results for the aboveground parts, with classification accuracy over 80% in training, validation and prediction set. The SVC models for Zhejing 88 presented better results than those for Xiushui 134, revealing the different herbicide tolerance between rice cultivars. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article' Conclusion:We develop a reliable and effective model using hyperspectral imaging technique which enables the evaluation and visualization of herbicide toxicity for rice. The reflectance spectra variations of rice could reveal the stress sta...

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“…The measurement is more precise compared to the measurement in conventional bulk rice grains, as the random air gap present in the bulk rice grains is excluded. Heman et al [ 18 ] proposed calibrated models for predicting three sample types of rough rice including single kernel based on near-infrared spectroscopy. The calibrated models for predicting moisture content can be applied to online machine development, but the technique is not easy to use for online applications due to the expensive setup and the fact that the accuracy of the model may be affected by the complex drying environment.…”

Section: Introductionmentioning

confidence: 99%

Developing an Online Measurement Device Based on Resistance Sensor for Measurement of Single Grain Moisture Content in Drying Process

Huang

et al. 2020

Sensors

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The online measurement of moisture content for grains is an essential technology to realize real-time tracking and control, improve drying quality and reduce energy consumption of the drying process. To improve the measurement accuracy and reliability of the dynamic measurement process as well as expand the application scope of the device, the present work constructed an experimental equipment for determining dynamic resistance characteristics of a single grain. The relations between moisture content and real-time resistance waveform were revealed, and an analytical calculation method of peak value and peak area of waveform was proposed, which correctly revealed the electrical measurement properties of grain. The results demonstrated that the gap width between the electrodes had large influence on the sensor’s performance. Moreover, an online measuring device was developed based on the experimental analysis and calculation method, and the test results in both lab and field for different grains showed that online real-time absolute measurement error are within ±0.5% in the varied moisture content (10–35%w.b.) and the temperature (−20–50 °C). The main results and the developed device might provide technical support for developing intelligent grain drying equipment.

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Measurement of moisture content for rough rice by visible and near-infrared (NIR) spectroscopy

Cited by 24 publications

References 10 publications

Determination of soybean routine quality parameters using near‐infrared spectroscopy

Determination of soybean routine quality parameters using near‐infrared spectroscopy

Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging

Developing an Online Measurement Device Based on Resistance Sensor for Measurement of Single Grain Moisture Content in Drying Process

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