Detection of Variety Contamination in Milled Japanese Rice Using a Single Kernel near Infrared Technique in Transmittance Mode

2005 ◽  
Vol 13 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Ronnarit Rittiron ◽  
Innapa Saranwong ◽  
Sumio Kawano
Keyword(s):  
Protein Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Measuring System ◽  
Protein Distribution ◽  
Milled Rice ◽  
Protein Determination ◽  
Fibre Optics ◽  
Calibration Equation ◽  
System A

An authentication system for identifying the purity of a milled rice bulk using near infrared (NIR) protein determination was established. NIR spectra from 1100 nm to 1800 nm of single kernels acquired with fibre optics in transmittance mode were used. First, the effect of the degree of milling (DOM) on the NIR protein determination was examined. Then, to obtain a robust and accurate protein measuring system, a calibration equation was developed with variety and DOM compensation. Finally, by examining the normal distribution of the protein content measured by the single kernel NIR spectroscopy, a blended bulk could be separated from a pure bulk as it did not exhibit a normal protein distribution while a pure one did. However, the system would have less capability if the average protein content of the contrasting variety was close to that of the main variety. In the naturally acquired protein histogram, the system proposed had the capability to detect the blending when the Koshihikari bulk included 5% or more Akitakomachi kernels.

Determination of Protein Content by NIR Spectroscopy in Protein Powder Mix Products

2016 ◽  
Vol 99 (2) ◽  
pp. 360-363 ◽  
Author(s):  
Prashant D Ingle ◽  
Roney Christian ◽  
Piyush Purohit ◽  
Veronica Zarraga ◽  
Erica Handley ◽  
...  
Keyword(s):  
Protein Content ◽  
Near Infrared ◽  
Low Cost ◽  
Rapid Test ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Combustion Method ◽  
Test Method ◽  
Health Food ◽  
Protein Determination

Abstract Protein is a principal component in commonly used dietary supplements and health food products. The analysis of these products, within the consumer package form, is of critical importance for the purpose of ensuring quality and supporting label claims. A rapid test method was developed using near-infrared (NIR) spectroscopy as a compliment to current protein determination by the Dumas combustion method. The NIR method was found to be a rapid, low-cost, and green (no use of chemicals and reagents) complimentary technique. The protein powder samples analyzed in this study were in the range of 22–90% protein. The samples were prepared as mixtures of soy protein, whey protein, and silicon dioxide ingredients, which are common in commercially sold protein powder drink-mix products in the market. A NIR regression model was developed with 17 samples within the constituent range and was validated with 20 independent samples of known protein levels (85–88%). The results show that the NIR method is capable of predicting the protein content with a bias of ±2% and a maximum bias of 3% between NIR and the external Dumas method.

scholarly journals Feasibility of Determination of Foodborne Microbe Contamination of Fresh-Cut Shredded Cabbage Using SW-NIR

2019 ◽  
Vol 1 (2) ◽  
pp. 246-256
Author(s):  
Benjamaporn Matulaprungsan ◽  
Chalermchai Wongs-Aree ◽  
Pathompong Penchaiya ◽  
Phonkrit Maniwara ◽  
Sirichai Kanlayanarat ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Sample Type ◽  
Least Squares Regression ◽  
Calibration Equation ◽  
E Coli ◽  
Total Bacteria

Shredded cabbage is widely used in much ready-to-eat food. Therefore, rapid methods for detecting and monitoring the contamination of foodborne microbes is essential. Short wavelength near infrared (SW-NIR) spectroscopy was applied on two types of solutions, a drained solution from the outer surface of the shredded cabbage (SC) and a ground solution of shredded cabbage (GC) which were inoculated with a mixture of two bacterial suspensions, Escherichia coli and Salmonella typhimurium. NIR spectra of around 700 to 1100 nm were collected from the samples after 0, 4, and 8 h at 37 °C incubation, along with the growth of total bacteria, E. coli and S. typhimurium. The raw spectra were obtained from both sample types, clearly separated with the increase of incubation time. The first derivative, a Savitzky–Golay pretreatment, was applied on the GC spectra, while the second derivative was applied on the SC spectra before developing the calibration equation, using partial least squares regression (PLS). The obtained correlation (r) of the SC spectra was higher than the GC spectra, while the standard error of cross-validation (SECV) was lower. The ratio of prediction of deviation (RPD) of the SC spectra was higher than the GC spectra, especially in total bacteria, quite normal for the E. coli but relatively low for the S. typhimurium. The prediction results of microbial spoilage were more reliable on the SC than on the GC spectra. Total bacterial detection was best for quantitative measurement, as E. coli contamination could only be distinguished between high and low values. Conversely, S. typhimurium predictions were not optimal for either sample type. The SW-NIR shows the feasibility for detecting the existence of microbes in the solution obtained from SC, but for a more specific application for discrimination or quantitation is needed, proving further research in still required.

Rapid analysis of chemical composition in intact and milled rice cookies using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 330-337 ◽  
Author(s):  
Latthika Wimonsiri ◽  
Pitiporn Ritthiruangdej ◽  
Sumaporn Kasemsumran ◽  
Nantawan Therdthai ◽  
Wasaporn Chanput ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Near Infrared ◽  
Wheat Gluten ◽  
Nir Spectroscopy ◽  
Rice Flour ◽  
Coefficient Of Determination ◽  
Gluten Free ◽  
Least Squares Regression ◽  
Milled Rice ◽  
Control Programs

This study has investigated the potential of near infrared (NIR) spectroscopy to predict the content of moisture, protein, fat and gluten in rice cookies in different sample forms (intact and milled samples). Gluten-free (n = 48) and gluten (n = 48) rice cookies were formulated with brown and white rice flours in which butter was substituted with fat replacer at 0, 15, 30 and 45%. With regard to gluten cookies, rice flour was substituted with wheat gluten at 1, 3 and 5%. Partial least squares regression modeling produced models with coefficient of determination (R2) values greater than 0.88 from NIR spectra of intact samples and greater than 0.92 for milled samples. These models were able to predict the four components with a ratio of prediction to deviation greater than 2.7 and 3.8 in intact and milled samples, respectively. The results suggest that the models obtained from the intact samples can be successfully applied for chemical composition of rice cookies and are reliable enough use for potential quality control programs.

scholarly journals Seed Mineral Composition and Protein Content of Faba Beans (Vicia faba L.) with Contrasting Tannin Contents

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 511 ◽  
Author(s):  
Hamid Khazaei ◽  
Albert Vandenberg
Keyword(s):  
Protein Content ◽  
Inductively Coupled Plasma ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Mineral Elements ◽  
Inductively Coupled ◽  
Faba Beans ◽  
Plasma Mass Spectrometry ◽  
High Concentrations ◽  
Mineral Concentrations

Two-thirds of the world’s population are at risk of deficiency in one or more essential mineral elements. The high concentrations of essential mineral elements in pulse seeds are fundamentally important to human and animal nutrition. In this study, seeds of 25 genotypes of faba bean (12 low-tannin and 13 normal-tannin genotypes) were evaluated for mineral nutrients and protein content in three locations in Western Canada during 2016–2017. Seed mineral concentrations were examined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and the protein content was determined by Near-Infrared (NIR) spectroscopy. Location and year (site-year) effects were significant for all studied minerals, with less effect for calcium (Ca) and protein content. Genotype by environment interactions were found to be small for magnesium (Mg), cobalt (Co), Ca, zinc (Zn), and protein content. Higher seed concentrations of Ca, manganese (Mn), Mg, and cadmium (Cd) were observed for low-tannin genotypes compared to tannin-containing genotypes. The protein content was 1.9% higher in low-tannin compared to tannin-containing genotypes. The high estimated heritability for concentrations of seed Mg, Ca, Mn, potassium (K), sulphur (S), and protein content in this species suggests that genetic improvement is possible for mineral elements.

A quick method based on SIMPLISMA-KPLS for simultaneously selecting outlier samples and informative samples for model standardization in near infrared spectroscopy

2017 ◽  
Vol 31 (34) ◽  
pp. 1750327
Author(s):  
Li-Na Li ◽  
Chang-Ming Ma ◽  
Ming Chang ◽  
Ren-Cheng Zhang
Keyword(s):  
Protein Content ◽  
Near Infrared ◽  
Selection Process ◽  
Sample Selection ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Quick Method ◽  
Model Transfer ◽  
Similar Accuracy

A novel method based on SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) and Kernel Partial Least Square (KPLS), named as SIMPLISMA-KPLS, is proposed in this paper for selection of outlier samples and informative samples simultaneously. It is a quick algorithm used to model standardization (or named as model transfer) in near infrared (NIR) spectroscopy. The NIR experiment data of the corn for analysis of the protein content is introduced to evaluate the proposed method. Piecewise direct standardization (PDS) is employed in model transfer. And the comparison of SIMPLISMA-PDS-KPLS and KS-PDS-KPLS is given in this research by discussion of the prediction accuracy of protein content and calculation speed of each algorithm. The conclusions include that SIMPLISMA-KPLS can be utilized as an alternative sample selection method for model transfer. Although it has similar accuracy to Kennard–Stone (KS), it is different from KS as it employs concentration information in selection program. This means that it ensures analyte information is involved in analysis, and the spectra (X) of the selected samples is interrelated with concentration (y). And it can be used for outlier sample elimination simultaneously by validation of calibration. According to the statistical data results of running time, it is clear that the sample selection process is more rapid when using KPLS. The quick algorithm of SIMPLISMA-KPLS is beneficial to improve the speed of online measurement using NIR spectroscopy.

Effect of Non-Peptide and Non-Protein Nitrogen Compounds for the Determination of Protein Content by near Infrared Spectroscopy

1994 ◽  
Vol 2 (3) ◽  
pp. 145-151 ◽  
Author(s):  
Hiromi Yamashita ◽  
Hitoshi Takamura ◽  
Teruyoshi Matoba
Keyword(s):  
Protein Content ◽  
Near Infrared ◽  
Accurate Determination ◽  
Nir Spectroscopy ◽  
Amide Group ◽  
Nitrogen Compounds ◽  
Protein Nitrogen ◽  
Peptide Bonds ◽  
Related Compounds

The significance of the absorption at 2170 nm due to peptide bonds for the determination of protein content by near infrared (NIR) spectroscopy was evaluated by comparing it with absorptions due to other nitrogens (non-peptide nitrogens in protein and non-protein nitrogens). The amide group (asparagine and glutamine), guanido group (arginine), imidazole group (histidine) and amino group (lysine) in proteins did not exhibit absorption at 2170 nm. The absorptions of nucleic acid related compounds also were not observed at 2170 nm. These results suggest that a wavelength of 2170 nm is suitable for the accurate determination of protein content in foods.

Prediction of Dry Matter and Crude Protein Content in Fresh Grass Silage by Near Infrared Spectroscopy

2005 ◽  
Vol 1 (1) ◽  
pp. 57-75 ◽  
Author(s):  
Marina Vranić ◽  
Mladen Knežević ◽  
Zsolt Seregély ◽  
Krešimir Bošnjak ◽  
Josip Leto ◽  
...  
Keyword(s):  
Protein Content ◽  
Crude Protein ◽  
Dry Matter ◽  
Near Infrared ◽  
Chemical Constituents ◽  
Nir Spectroscopy ◽  
Meat Production ◽  
Crude Protein Content ◽  
Near Infrared Reflectance ◽  
Grass Silage

Intensive livestock feeding requires constant monitoring of diet composition to ensure a consistent level of milk or meat production. A rapid analysis of fresh grass silage samples for dry matter (DM) and crude protein (CP) content would provide basic, rapid information what would permit decision to be made regarding the need to supplement the diet. The aim of the present study was to determine dry matter (DM) and crude protein (CP) content in fresh grass silage samples by NIR spectroscopy. Crude protein content can be expressed as g per kg dry matter (g kg-1 DM) or as per cent of fresh weight (% FW). Near-infrared reflectance spectra were recorded for 103 fresh grass silage samples. Laboratory analysis of DM and CP were determined for these samples. MLR, PCR and PLS techniques were used for data modelling to determine the optimum models for predicting each of the chemical constituents. It was concluded that the PLS method was superior to the PCR and MLR methods for DM and CP prediction in fresh grass silage samples, while MLR showed a promising possibility to determine the CP content using only two spectral values measured at two “characteristic”wavelengths.

scholarly journals Predicting rapeseed oil content with near-infrared spectroscopy

2013 ◽  
Vol 48 (12) ◽  
pp. 1601-1605 ◽  
Author(s):  
Roberta Rossato ◽  
Cássio Egídio Cavenaghi Prete ◽  
César de Castro ◽  
Gilberto Omar Tomm ◽  
Rodrigo Santos Leite ◽  
...  
Keyword(s):  
Rapeseed Oil ◽  
Chemical Method ◽  
Oil Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Calibration Equation ◽  
Sib Families ◽  
The One

The objective of this work was to establish a calibration equation and to estimate the efficiency of near-infrared reflectance (NIR) spectroscopy for evaluating rapeseed oil content in Southern Brazil. Spectral data from 124 half-sib families were correlated with oil contents determined by the chemical method. The accuracy of the equation was verified by coefficient of determination (R²) of 0.92, error of calibration (SEC) of 0.78, and error of performance (SEP) of 1.22. The oil content of ten genotypes, which were not included in the calibration with NIR, was similar to the one obtained by the standard chemical method. NIR spectroscopy is adequate to differentiate oil content of rapeseed genotypes.

Development of near Infrared Analysis of Faeces to Estimate Non-Grass Proportions in Diets Selected by Cattle Grazing Tropical Pastures

2008 ◽  
Vol 16 (5) ◽  
pp. 471-480 ◽  
Author(s):  
David B. Coates ◽  
Rob M. Dixon
Keyword(s):  
Standard Error ◽  
Nutritive Value ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Grass Species ◽  
Infrared Analysis ◽  
Tropical Pastures ◽  
Calibration Equation ◽  
Ruminant Diets ◽  
Modified Formula

Grass (monocots) and non-grass (dicots) proportions in ruminant diets are important nutritionally because the non-grasses are usually higher in nutritive value, particularly protein, than the grasses, especially in tropical pastures. For ruminants grazing tropical pastures where the grasses are C4 species and most non-grasses are C3 species, the ratio of 13C/12C in diet and faeces, measured as δ13C‰, is proportional to dietary non-grass%. This paper describes the development of a faecal near infrared (NIR) spectroscopy calibration equation for predicting faecal δ13C from which dietary grass and non-grass proportions can be calculated. Calibration development used cattle faeces derived from diets containing only C3 non-grass and C4 grass components, and a series of expansion and validation steps was employed to develop robustness and predictive reliability. The final calibration equation contained 1637 samples and faecal δ13C range (‰) of [12.27]–[27.65]. Calibration statistics were: standard error of calibration (SEC) of 0.78, standard error of cross-validation (SECV) of 0.80, standard deviation ( SD) of reference values of 3.11 and R2 of 0.94. Validation statistics for the final calibration equation applied to 60 samples were: standard error of prediction ( SEP) of 0.87, bias of −0.15, R2 of 0.92 and RPD of 3.16. The calibration equation was also tested on faeces from diets containing C4 non-grass species or temperate C3 grass species. Faecal δ13C predictions indicated that the spectral basis of the calibration was not related to 13C/12C ratios per se but to consistent differences between grasses and non-grasses in chemical composition and that the differences were modified by photosynthetic pathway. Thus, although the calibration equation could not be used to make valid faecal δ13C predictions when the diet contained either C3 grass or C4 non-grass, it could be used to make useful estimates of dietary non-grass proportions. It could also be used to make useful estimates of non-grass in mixed C3 grass/non-grass diets by applying a modified formula to calculate non-grass from predicted faecal δ13C. The development of a robust faecal-NIR calibration equation for estimating non-grass proportions in the diets of grazing cattle demonstrated a novel and useful application of NIR spectroscopy in agriculture.

Measurement of the Concentrations of Mannosyl Erythritol Lipid and Soybean Oil in the Glycolipid Fermentation Process Using near Infrared Spectroscopy

2002 ◽  
Vol 10 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Kazuhiro Nakamichi ◽  
Ken-Ichiro Suehara ◽  
Yasuhisa Nakano ◽  
Koji Kakugawa ◽  
Masahiro Tamai ◽  
...  
Keyword(s):  
Soybean Oil ◽  
Ethyl Acetate ◽  
Spectral Data ◽  
Standard Error ◽  
Fermentation Process ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Raw Material ◽  
Second Derivative ◽  
Calibration Equation

In a glycolipid fermentation, mannosyl erythritol lipid (MEL) is produced from soybean oil added to a medium as a source of carbon. A measurement system for the concentrations of MEL and soybean oil in the fermentation process has been developed using near infrared (NIR) spectroscopy. MEL and soybean oil in the culture broth were extracted with ethyl acetate. NIR spectra of the ethyl acetate extract were measured in the wavelength range between 400 and 2500 nm at 2 nm intervals. The absorption caused by MEL was observed at 1436, 1920 and 2052 nm. To obtain a calibration equation, a multiple linear regression (MLR) was carried out between the second derivative NIR spectral data at 2040 and 1312 nm and MEL concentrations obtained using thin-layer chromatography with a flame-ionisation detector (TLC/FID) method. The values of the regression coefficient ( R) and the standard error of calibration ( SEC) were 0.994 and 0.48 g L−1, respectively. The absorption caused by soybean oil was observed at 1208, 1716, 1766, 2182 and 2302 nm. A calibration equation for soybean oil was formulated with the second derivative NIR spectral data at 2178 and 2090 nm. The values of R and SEC were 0.974 and 0.77 g L−1, respectively. After validation of the calibration equation, good agreement was observed between the results of the TLC/FID method and those of the NIR method for both constituents. The values of the correlation coefficient ( r) for MEL and the standard error of prediction ( SEP) were 0.994 and 0.45 g L−1, respectively. The values of r and SEP for soybean oil were 0.979 and 0.56 g L−1, respectively. The NIR method was applied to the measurement of the concentrations of MEL and soybean oil in an actual fermentation process and good results were obtained. The study indicates that NIR spectroscopy is a useful method for the measurement of the raw material and product in glycolipid fermentation.

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