scholarly journals Feasibility study on estimation of rice weevil quantity in rice stock using near-infrared spectroscopy technique

2014 ◽  
Vol 07 (04) ◽  
pp. 1450001 ◽  
Author(s):  
Puttinun Jarruwat ◽  
Prasan Choomjaihan
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fourier Transforms ◽  
Rice Variety ◽  
Brown Rice ◽  
Partial Least Square ◽  
The Body ◽  
Rice Weevil ◽  
Low Grade

Thai rice is favored by large numbers of consumers of all continents because of its excellent taste, fragrant aroma and fine texture. Among all Thai rice varieties, Thai Hommali rice is the most preferred. Classification of rice as premium quality requires that almost all grain kernels of the rice be perfectly whole with only a small quantity of foreign particles. Of all the foreign particles found in rice, rice weevils can wreck severest havoc on the quality and quantity of rice such that premium grade rice is transformed into low grade rice. It is widely known that rice millers adopt the "overdose" fumigation practice to control the birth and propagation of rice weevils, the practice of which inevitably gives rise to pesticide residues on rice which end up in the body of consumers. However, if population concentration of rice weevils could be approximated, right amounts of chemicals for fumigation would be applied and thereby no overdose is required. The objective of this study is thus to estimate the quantity of rice weevils in both milled rice and brown rice of Thai Hommali rice variety using the near infrared spectroscopy (NIRS) technique. Fourier transforms near infrared (FT-NIR) spectrometer was used in this research and the near-infrared wavelength range was 780–2500 nm. A total of 20 levels of rice weevil infestation with an increment of 10 from 10 to 200 mature rice weevils were applied to 1680 rice samples. The spectral data and quantity of weevils are analyzed by partial least square regression (PLSR) to establish the model for prediction. The results show that the model is able to estimate the quantity of weevils in milled Hommali rice and brown Hommali rice with high [Formula: see text] of 0.96 and 0.90, high RPD of 6.07 and 3.26 and small bias of 2.93 and 2.94, respectively.

scholarly journals Study on evaluation of gamma oryzanol of germinated brown rice by near infrared spectroscopy

2014 ◽  
Vol 07 (04) ◽  
pp. 1450002 ◽  
Author(s):  
Kannapot Kaewsorn ◽  
Panmanas Sirisomboon
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dry Matter ◽  
Near Infrared ◽  
Cross Validation ◽  
Brown Rice ◽  
Partial Least Square ◽  
Germinated Brown Rice ◽  
Rice Samples ◽  
Gamma Oryzanol

Germinated brown rice (GBR) is rich in gamma oryzanol which increase its consumption popularity, particularly in the health food market. The objective of this research was to apply the near infrared spectroscopy (NIRS) for evaluation of gamma oryzanol of the germinated brown rice. The germinated brown rice samples were prepared from germinated rough rice (soaked for 24 and 48 h, incubated for 0, 6, 12, 18, 24, 30 and 36 h) and purchased from local supermarkets. The germinated brown rice samples were subjected to NIR scanning before the evaluation of gamma oryzanol by using partial extraction methodology. The prediction model was established by partial least square regression (PLSR) and validated by full cross validation method. The NIRS model established from various varieties of germinated brown rice bought from different markets by first derivatives+vector normalization pretreated spectra showed the optimal prediction with the correlation of determination (R2), root mean squared error of cross validation (RMSECV) and bias of 0.934, 8.84 × 10-5 mg/100 g dry matter and 1.06 × 10-5 mg/100 g dry matter, respectively. This is the first report on the application of NIRS in the evaluation of gamma oryzanol of the germinated brown rice. This information is very useful to the germinated brown rice production factory and consumers.

Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xuyang Pan ◽  
Laijun Sun ◽  
Guobing Sun ◽  
Panxiang Rong ◽  
Yuncai Lu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Prediction Model ◽  
Spectral Data ◽  
Corn Stover ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Fiber Content ◽  
Partial Least Square ◽  
Neutral Detergent Fiber

AbstractNeutral detergent fiber (NDF) content was the critical indicator of fiber in corn stover. This study aimed to develop a prediction model to precisely measure NDF content in corn stover using near-infrared spectroscopy (NIRS) technique. Here, spectral data ranging from 400 to 2500 nm were obtained by scanning 530 samples, and Monte Carlo Cross Validation and the pretreatment were used to preprocess the original spectra. Moreover, the interval partial least square (iPLS) was employed to extract feature wavebands to reduce data computation. The PLSR model was built using two spectral regions, and it was evaluated with the coefficient of determination (R2) and root mean square error of cross validation (RMSECV) obtaining 0.97 and 0.65%, respectively. The overall results proved that the developed prediction model coupled with spectral data analysis provides a set of theoretical foundations for NIRS techniques application on measuring fiber content in corn stover.

scholarly journals Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat

2021 ◽  
Vol 13 (6) ◽  
pp. 1128
Author(s):  
Iman Tahmasbian ◽  
Natalie K Morgan ◽  
Shahla Hosseini Bai ◽  
Mark W Dunlop ◽  
Amy F Moss
Keyword(s):  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Benefits ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Population ◽  
Processing Plants ◽  
C And N

Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS Rc2 for C = 0.90 and N = 0.96 vs. HSI Rc2 for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted.

scholarly journals Discrimination and Content Analysis of Fritillaria Using Near Infrared Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yu Meng ◽  
Shisheng Wang ◽  
Rui Cai ◽  
Bohai Jiang ◽  
Weijie Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Traditional Chinese Herbal Medicine ◽  
Total Alkaloids ◽  
Rapid Discrimination ◽  
Different Origins

Fritillaria is a traditional Chinese herbal medicine which can be used to moisten the lungs. The objective of this study is to develop simple, accurate, and solvent-free methods to discriminate and quantify Fritillaria herbs from seven different origins. Near infrared spectroscopy (NIRS) methods are established for the rapid discrimination of seven different Fritillaria samples and quantitative analysis of their total alkaloids. The scaling to first range method and the partial least square (PLS) method are used for the establishment of qualitative and quantitative analysis models. As a result of evaluation for the qualitative NIR model, the selectivity values between groups are always above 2, and the mistaken judgment rate of fifteen samples in prediction sets was zero. This means that the NIR model can be used to distinguish different species of Fritillaria herbs. The established quantitative NIR model can accurately predict the content of total alkaloids from Fritillaria samples.

Near-Infrared Spectroscopy for Discrimination of Charcoal from Eucalyptus and Native Cerrado Species—Contribution to a Database for Forestry Supervision

2021 ◽  
Author(s):  
Silvana Nisgoski ◽  
Thaís A P Gonçalves ◽  
Júlia Sonsin-Oliveira ◽  
Adriano W Ballarin ◽  
Graciela I B Muñiz
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
New Technologies ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Eucalyptus Species ◽  
Linear Discriminant

Abstract The illegal charcoal trade is an internationally well-known forest crime. In Brazil, government agents try to control it using the document of forest origin (DOF). To confirm a load’s legality, the agents must compare it with the declared content of the DOF. However, to identify charcoal is difficult even for specialists in wood anatomy. Hence, new technologies would facilitate the agents’ work. Near-infrared spectroscopy (NIR) provides a rapid and precise response to differentiate carbonized species. Considering the rich Brazilian flora, NIR studies are still underdeveloped. Our work aimed to differentiate charcoals of seven eucalypts and 10 Cerrado species based on NIR analysis and to add information to a charcoal database. Data were collected with a spectrophotometer in reflectance mode. Partial least square regression with discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA) was applied to confirm the performance and potential of NIR spectra to distinguish native Cerrado species from eucalyptus species. Wavenumbers from 4,000 to 6,000 cm−1 and transversal surface presented the best results. NIR had the potential to distinguish eucalypt charcoals from Cerrado species and in comparison to reference samples. NIR is a potential tool for forestry supervision to guarantee the sustainability of the charcoal supply in Brazil and countries with similar conditions. Study Implications It is a challenge to protect the Cerrado biome against deforestation for charcoal production. The application of new technologies such as near-infrared spectroscopy (NIR) for charcoal identification might improve the work of government agents. In this article, we studied the spectra of Cerrado and eucalypt species. Our results present good separation between the analyzed groups. The main goal is to develop a reliable NIR database that would be useful in the practical work of agents. The database will be available for all control agencies, and future training will be done for a rapid initial evaluation in the field.

scholarly journals Use of near infrared spectroscopy and chemometrics to evaluate the shelf-life of cloudy sonicated apple juice

2019 ◽  
Vol 27 (1) ◽  
pp. 75-85 ◽  
Author(s):  
Lorenzo Serva ◽  
Stefania Balzan ◽  
Vittoria Bisutti ◽  
Filomena Montemurro ◽  
Giorgio Marchesini ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Shelf Life ◽  
Near Infrared Spectroscopy ◽  
Storage Time ◽  
Apple Juice ◽  
Near Infrared ◽  
Partial Least Square ◽  
Feasibility Trial ◽  
End Point

Fresh products, such as cloudy apple juice, could be preserved from early spoilage through the application of non-thermal processes such as sonication. However, shelf-life analyses based on microbiological and sensory evaluations are expensive and time consuming. Few studies have applied near infrared spectroscopy to evaluate the quality and decay of apple juices. Here, a feasibility trial was conducted to study the spectral behaviour at 1300–2500 nm combined with chemometric approaches. The shelf-life was monitored during two experiments, a challenge test with juices inoculated with spoilage yeasts (inoculated non-sonicated (INS)) and then submitted to sonication treatments (inoculated sonicated (IS)), and a storage test to evaluate the spoilage on non-inoculated juices (non-inoculated non-sonicated (NINS)) and sonicated non-inoculated juices (non-inoculated sonicated (NIS)). These experiments were investigated at six different refrigeration times 7, 14, 21, 28 and 60 days. Two functions were modelled to describe the behaviours of the first principal component according to the storage time. In agreement with a previous chemical and sensory evaluation, this approach allowed us to highlight shelf-life end points of 7 and 14 days for non-sonicated and sonicated samples, respectively. Three different models were evaluated for classification purposes: (1) sonicated versus non-treated samples, (2) end-point shelf-life evaluation at seven days for the NINS and INS juices and (3) end-point shelf-life discrimination at 14 days for IS and NIS samples. A partial least square-discriminant analysis enabled a group classification with accuracy values ranging from 0.63 to 1.00. The application of a variable importance in projection index to interpret the wavelengths of the spectral features suggests a contribution of organic acids and lipids to the prediction of decay. A canonical discriminant analysis provided a clearer separation of samples according to the storage time, especially in relation to the two time thresholds of 7 and 14 days.

Using near infrared spectroscopy to determine moisture and starch content of corn processing products

2017 ◽  
Vol 25 (5) ◽  
pp. 348-359 ◽  
Author(s):  
Ye Chen ◽  
Lauren Delaney ◽  
Susan Johnson ◽  
Paige Wendland ◽  
Rogerio Prata
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Starch Content ◽  
Rapid Development ◽  
Partial Least Square ◽  
Least Square ◽  
Operational Parameters ◽  
Corn Flour ◽  
Analytical Technique

Due to the rapid development of the corn-to-ethanol industry, the demand for process monitoring has led to the gradual substitution of traditional analytical techniques with fast and non-destructive methods such as near infrared spectroscopy. In this study, the feasibility of using Fourier transform–near infrared technology as an analytical tool to predict operational parameters (dry solids, starch, carbohydrate, and ethanol content) was investigated. Corn flour, liquefied mash, fermented mash, and distiller’s dried grains with solubles were selected to represent the feedstock, two intermediate products, and one primary co-product of corn-to-ethanol plants, respectively. Multivariate partial least square calibration models were developed to correlate near infrared spectra to the corresponding analytical values. The validation results indicate that near infrared models can be developed that will predict various parameters accurately (root mean square error of prediction: 0.16–1.14%, residual predictive deviation: 3.0–6.3). Measurement of starch or carbohydrate content in corn flour or distiller’s dried grains with solubles is challenging due to low accuracy of wet chemistry methods as well as sample complexity. The study demonstrated that near infrared spectroscopy, a high-throughput analytical technique, has the potential to replace the enzymatic assay.

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