Eating quality of cooked rice determination using Fourier transform near infrared spectroscopy

The goal of this research was to study the relationship between the eating quality of cooked rice and near infrared spectra measured by a Fourier Transform near infrared (FT–NIR) Spectrometer. Samples of milled: parboiled rice, white rice, new Jasmine rice (harvested in 2012) and aged Jasmine rice (harvested in 2006 or during the period 2007–2011) were used in this study. The eating quality of the cooked rice, i.e., adhesiveness, hardness, dryness, whiteness and aroma, were evaluated by trained sensory panelists. FT–NIR spectroscopy models for predicting the eating quality of cooked rice were established using the partial least squares regression. Among the eating quality, the stickiness model indicated its highest prediction ability (i.e., [Formula: see text]; RMSEP = 0.65; Bias = 0.00; RPD = 1.87) and SEP/SD of 2. In addition, it was clear that the water content did not affect the eating quality of cooked rice, rather the main chemical component implicated was starch.

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Relationship between Sensory Textural Attributes and near Infrared Spectra of Cooked Rice

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.746 ◽

2007 ◽

Vol 15 (5) ◽

pp. 333-340 ◽

Cited By ~ 7

Author(s):

Weena Srisawas ◽

Vinod K. Jindal ◽

Warunee Thanapase

Keyword(s):

Near Infrared ◽

Profile Analysis ◽

Indica Rice ◽

Nir Spectroscopy ◽

Texture Profile Analysis ◽

Eating Quality ◽

Least Squares Regression ◽

Texture Profile ◽

Cooked Rice ◽

Instrumental Texture

Fourteen varieties of Thai indica rice, cooked with five water-to-rice ratios ranging from 1.3 to 2.5 on a weight basis, were characterised by sensory and instrumental texture profile analysis. The potential of near infrared (NIR) reflectance spectroscopy was investigated as an alternative tool for evaluating eating quality attributes of cooked rice by developing predictive models for sensory hardness, stickiness and glossiness. Partial least squares regression models were developed which predicted sensory hardness and stickiness slightly better than the glossiness with r2v values ranging from 0.88 to 0.91 and standard errors of prediction ( SEP) lower than 0.4 unit score on nine-point sensory intensity scales. Results indicated that NIR spectroscopy-based models could be used for estimating the sensory hardness, stickiness and glossiness scores of cooked rice with higher accuracy (lower SEP) compared to the instrumental texture profile analysis based-models.

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Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation

Journal of Near Infrared Spectroscopy ◽

10.1177/09670335211057235 ◽

2022 ◽

pp. 096703352110572

Author(s):

Nicholas T Anderson ◽

Kerry B Walsh

Keyword(s):

Near Infrared ◽

Nir Spectroscopy ◽

Short Wave ◽

Least Squares Regression ◽

Machine Learning Methods ◽

Transmission Geometry ◽

Modelling Techniques ◽

Test Sets ◽

Model Robustness

Short wave near infrared (NIR) spectroscopy operated in a partial or full transmission geometry and a point spectroscopy mode has been increasingly adopted for evaluation of quality of intact fruit, both on-tree and on-packing lines. The evolution in hardware has been paralleled by an evolution in the modelling techniques employed. This review documents the range of spectral pre-treatments and modelling techniques employed for this application. Over the last three decades, there has been a shift from use of multiple linear regression to partial least squares regression. Attention to model robustness across seasons and instruments has driven a shift to machine learning methods such as artificial neural networks and deep learning in recent years, with this shift enabled by the availability of large and diverse training and test sets.

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Evaluation of the physicochemical content and solid-state fermentation stage of Zhenjiang aromatic vinegar using near-infrared spectroscopy

International Journal of Food Engineering ◽

10.1515/ijfe-2020-0127 ◽

2020 ◽

Vol 16 (12) ◽

Author(s):

Suwan Fan ◽

Tianhong Pan ◽

Guoquan Li

Keyword(s):

Solid State ◽

Solid State Fermentation ◽

Near Infrared ◽

Evaluation Method ◽

Nir Spectroscopy ◽

Least Squares Regression ◽

Total Acid ◽

Fermentation Products ◽

Fermentation Stage

AbstractAs one of the most famous traditional Chinese vinegars, the grains physicochemical content of Zhenjiang aromatic vinegar during solid-state fermentation (SSF) reflects the growth status of microorganisms and the quality of fermentation products. In addition, the time for grain-turning has a significant effect on the quality of fermentation products. In this study, a new evaluation method combined near-infrared (NIR) spectroscopy with partial least squares regression (PLSR) was proposed to predict the physicochemical content of grains and the fermentation stage. The performance of the PLSR models for the total acid and the nonvolatile acid were RMSEP = 0.0371, Rp2 = 0.9760, and RMSEP = 0.0216, Rp2 = 0.9646, respectively. The accuracy ratio of SSF stage judgment was 100%. Experimental results indicate that the proposed method can be used to guide on-site grain-turning and improve the quality of fermentation products.

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Applying NIR spectroscopy to evaluate quality of whey protein supplements available on the Polish market

Zywnosc Nauka Technologia Jakosc/Food Science Technology Quality ◽

10.15193/zntj/2018/115/233 ◽

2018 ◽

Vol 115 (2) ◽

pp. 59-70

Author(s):

Krzysztof Wójcicki

Keyword(s):

Near Infrared ◽

Protein Hydrolysate ◽

Scatter Correction ◽

Protein A ◽

Nir Spectroscopy ◽

Principal Component ◽

Protein Isolate ◽

Least Squares Regression ◽

Protein Supplements

The objective of the research study was to apply near infrared (NIR) spectroscopy to evaluate the quality of protein supplements available in the Polish shops and gyms. The evaluation was performed on the basis of the determination of the protein quantity contained in the individual samples by a Kjeldahl method and then the evaluation results were correlated with the measured NIR spectra using an appropriate chemometric method. The research material consisted of fifteen protein supplement samples for athletes, which included the following types: WPI (protein isolate), WPC (protein concentrate), WPH (protein hydrolysate), and mixtures thereof. The obtained NIR spectra of protein supplements were characterized by a similar shape of the bands. Depending on the type of protein, a different intensity of absorption of individual bands could be observed. A Principal Component Analysis (PCA) was used to distinguish the samples based on the spectra measured. Unfortunately, owing to the varying composition of the protein mixtures, it was not possible to find characteristic arrangement of the samples depending on their types. The spectra were correlated with the protein contents determined in the samples using a Partial Least Squares regression method (PLS regression) and various mathematic transformations of the NIR spectral data. The obtained regression models were analysed and the analysis results confirmed that it was possible to apply NIR spectra to estimate the content of proteins in protein supplements. The best result was obtained in a spectrum region between 9401 and 5448 cm-1 and after the first derivative was applied with Multiplicate Scatter Correction (MSC) as a mathematical pre-treatment. On the basis of the results obtained, it was proved that the NIR spectra applied together with the chemometric analysis could be used to quickly evaluate the products studied.

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Hydraulic traits of Norway spruce sapwood estimated by Fourier transform near-infrared spectroscopy (FT-NIR)

Canadian Journal of Forest Research ◽

10.1139/cjfr-2014-0452 ◽

2015 ◽

Vol 45 (6) ◽

pp. 625-631 ◽

Cited By ~ 4

Author(s):

Saskia Luss ◽

Manfred Schwanninger ◽

Sabine Rosner

Keyword(s):

Fourier Transform ◽

Norway Spruce ◽

Hydraulic Properties ◽

Near Infrared ◽

Cross Validation ◽

Nir Spectroscopy ◽

Air Pressure ◽

Coefficient Of Determination ◽

Least Squares Regression ◽

The Impact

The potential of Fourier transform near-infrared (FT-NIR) spectroscopy to predict hydraulic traits in Norway spruce (Picea abies (L.) Karst.) sapwood was evaluated. Hydraulic traits tested were P50 (applied air pressure causing 50% loss of hydraulic conductivity) and RWL50 (applied air pressure causing 50% relative water loss). Samples came from 24-year-old spruce clones. FT-NIR spectra were collected from the axial (transverse) and radial surface of each solid wood sample for the prediction of P50 and RWL50. Partial least squares regression (PLS-R) models with cross validation were used to establish relationships between the FT-NIR spectra and the reference data from hydraulic properties analysis. The impact of the wavenumber range and the pretreatment during the PLS-R model development and the differences between the axial and radial surfaces were shown. Based on the values of the coefficient of determination (r2) and the root mean square error of cross validation, predicted results were evaluated as acceptable. The models from the axial surface gave better results than the models from the radial surface for P50 (r2 = 0.65), as well as for RWL50 (r2 = 0.77). The first approach to predict hydraulic properties such as P50 and RWL50 by FT-NIR spectroscopy can be regarded as successful. We conclude that the method has high potential to be put into practice as a rapid, reliable, and nondestructive method to determine P50 and RWL50.

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Monitoring the Quality of Oils for Biodiesel Production Using Multivariate near Infrared Spectroscopy Models

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.814 ◽

2008 ◽

Vol 16 (5) ◽

pp. 445-454 ◽

Cited By ~ 21

Author(s):

Patrícia Baptista ◽

Pedro Felizardo ◽

José C. Menezes ◽

M. Joana Neiva Correia

Keyword(s):

Quality Control ◽

Water Content ◽

Vegetable Oils ◽

Iodine Value ◽

Near Infrared ◽

Nir Spectroscopy ◽

Acid Number ◽

Biodiesel Production ◽

Least Squares Regression

Biodiesel is a mixture of fatty acid methyl esters, derived from vegetable oils or animal fats, which is usually produced by a transesterification reaction, where the oils or fats react with an alcohol in the presence of a catalyst. The quality of the oils used for biodiesel production strongly influences the final properties of biodiesel, namely its compliance to the European Standard. This work reports the use of near infrared (NIR) spectroscopy in the quality control of several oil properties, such as the iodine value, the water content and the acid number but, more importantly, the weight–weight percentages (wt%) of soybean, palm and rapeseed oil in mixtures. Principal component analysis was used to perform a qualitative analysis of the spectra, whereas partial least squares regression allowed the development of calibration models between analytical reference data and NIR spectra. The calibration ranges were 60–126 g I2 100 g−1 for the iodine value, 478–2500 mg kg−1 for the water content and 0.13-6.56 mg KOH g−1 for the acid number, whereas the validation errors were around 3.1 g I2 100 g−1, 111 mg kg−1 and 0.22 mg KOH g−1, respectively. The results obtained show that NIR spectroscopy is a promising technique to carry out the quality control of the commonly used vegetable oils for biodiesel production, namely the quality assurance and authenticity. Furthermore, it is of great value to have a simple, fast and reliable method to identify the composition of an oil mixture and/or some of its quality parameters, prior to storage or upon admission of a new lot of oil.

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Rapid Identification and Quality Evaluation of Medicinal Centipedes in China Using Near-Infrared Spectroscopy Integrated with Support Vector Machine Algorithm

Journal of Spectroscopy ◽

10.1155/2019/9636823 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11

Author(s):

Sihe Kang ◽

Haiying Deng ◽

Long Chen ◽

Xiaoxuan Zeng ◽

Yimei Liu ◽

...

Keyword(s):

Quantitative Analysis ◽

Nitrogen Content ◽

Quality Evaluation ◽

Near Infrared ◽

Nir Spectroscopy ◽

Rapid Identification ◽

Support Vector ◽

Prediction Ability ◽

Analysis Models

To investigate the feasibility of rapid identification and quality evaluation of Chinese medicinal centipedes using NIR spectroscopy, the qualitative and quantitative analysis models were explored. A PCA-SVC model was optimized to differentiate five species of the genus Scolopendra. When the model was validated with the calibration and prediction sets, the prediction accuracy was 100% and 81.82%, respectively; it can meet the requirement for rapid and preliminary identification. Based on nitrogen content detected by the chemical method, and the dimensionality of spectral data reduced with PLS, the quantitative analysis models were successfully built by PLSR and SVR algorithms. After spectra were pretreated and parameters were optimized, the performance, rationality, and prediction ability of the models were validated and evaluated with RMSECV, RMSEP, RMSEE, R2, and RPD. Compared with the features and advantages of these two models, the PLS-SVR model had better performance and stronger prediction capacity, and it was finally regarded as the optimal quantitative analysis model to predict nitrogen content. The relative deviation between the predictive value and the reference was 2.69%, and the average recovery was 99.02%, which indicated it has potential for rapid prediction and evaluation of the quality of medicinal centipedes. This research suggested that NIR spectroscopy can be used as a rapid detection method to identify species and evaluate the quality of medicinal centipedes in China.

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Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

Recycling ◽

10.3390/recycling6010011 ◽

2021 ◽

Vol 6 (1) ◽

pp. 11

Author(s):

Kirsti Cura ◽

Niko Rintala ◽

Taina Kamppuri ◽

Eetta Saarimäki ◽

Pirjo Heikkilä

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Automated Recognition ◽

Textile Materials ◽

Input Material ◽

Adequate Quality ◽

Automated Line

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

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Rapid Determination of Heartwood Extractives in Larix sp. by Means of Fourier Transform near Infrared Spectroscopy

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.336 ◽

2002 ◽

Vol 10 (3) ◽

pp. 203-214 ◽

Cited By ~ 69

Author(s):

N. Gierlinger ◽

M. Schwanninger ◽

B. Hinterstoisser ◽

R. Wimmer

Keyword(s):

Fourier Transform ◽

Near Infrared ◽

Cross Validation ◽

Nir Spectroscopy ◽

Solid Wood ◽

Hot Water ◽

Extractive Content ◽

Fast Method ◽

Wood Powder ◽

Test Set

The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy to rapidly determine extractive and phenolic content in heartwood of larch trees ( Larix decidua MILL., L. leptolepis (LAMB.) CARR. and the hybrid L. x eurolepis) was investigated. FT-NIR spectra were collected from wood powder and solid wood using a fibre-optic probe. Partial Least Squares (PLS) regression analyses were carried out describing relationships between the data sets of wet laboratory chemical data and the FT-NIR spectra. Besides cross and test set validation the established models were subjected to a further evaluation step by means of additional wood samples with unknown extractive content. Extractive and phenol contents of these additional samples were predicted and outliers detected through Mahalanobis distance calculations. Models based on the whole spectral range and without data pre-processing performed well in cross-validation and test set validation, but failed in the evaluation test, which is based on spectral outlier detection. But selection of data pre-processing methods and manual as well as automatic restriction of wavenumber ranges considerably improved the model predictability. High coefficients of determination ( R2) and low root mean square errors of cross-validation ( RMSECV) were obtained for hot water extractives ( R2 = 0.96, RMSECV = 0.86%, range = 4.9–20.4%), acetone extractives ( R2 = 0.86, RMSECV = 0.32%, range = 0.8–3.6%) and phenolic substances ( R2 = 0.98, RMSECV = 0.21%, range = 0.7–4.9%) from wood powder. The models derived from wood powder spectra were more precise than those obtained from solid wood strips. Overall, NIR spectroscopy has proven to be an easy to facilitate, reliable, accurate and fast method for non-destructive wood extractive determination.

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Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

10.5194/egusphere-egu21-3896 ◽

2021 ◽

Author(s):

Iva Hrelja ◽

Ivana Šestak ◽

Igor Bogunović

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Spectral Data ◽

Near Infrared ◽

Mean Squared Error ◽

Nir Spectroscopy ◽

Soil Samples ◽

Least Squares Regression ◽

Macro Scale ◽

Sentinel 2

Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.The study is performed in Mediterranean Croatia (44&#176; 05&#8217; N; 15&#176; 22&#8217; E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed Quercus ssp. and Juniperus ssp. forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22nd 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31st and September 5th 2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSEp) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSEp (Spectroscopy dataset RMSEp = 1.91; Sentinel-2 dataset RMSEp = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.Keywords: remote sensing, soil spectroscopy, wildfires, soil organic matterAcknowledgment: This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Per&#269;in is acknowledged for her cooperation during the laboratory work.

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