scholarly journals Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy

2019 ◽  
Vol 9 (8) ◽  
pp. 1654 ◽  
Author(s):  
Lei Lin ◽  
Yong He ◽  
Zhitao Xiao ◽  
Ke Zhao ◽  
Tao Dong ◽  
...  
Keyword(s):  
Moisture Content ◽  
Rapid Detection ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Rice Grain ◽  
Grain Moisture ◽  
And Storage

Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral data to find the rice grain moisture NIR spectroscopy. As a result, the 1450 nm band was sensitive to the rice grain moisture and a rapid-detection sensor was developed with a 1450 nm light emitting diode (LED) light source, InGaAs photodiode, lens and filter, whose basic principle is to establish the relationship between the rice grain moisture and the measured voltage signal. To evaluate the sensor performance, rice grain samples with 13–30% moisture content were detected, the coefficient of determination R2 was 0.936, and the sum of squares for error (SSE) was 23.44. It is concluded that this study provides a spectroscopic measuring method, as well as developing an effective and accurate sensor for the rapid determination of rice grain moisture, which is of great significance for monitoring the quality of rice grain during its production, transportation and storage process.

Application of near Infrared Spectroscopy On-Combine in Corn Grain Breeding

2005 ◽  
Vol 13 (2) ◽  
pp. 69-75 ◽  
Author(s):  
Roland Welle ◽  
Willi Greten ◽  
Thomas Müller ◽  
Gary Weber ◽  
Hartwig Wehrmann
Keyword(s):  
Grain Yield ◽  
Dry Matter ◽  
Near Infrared ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Successful Implementation ◽  
Grain Moisture ◽  
Yield Determination ◽  
Corn Grain

Improving maize ( Zea mays L.) grain yield and agronomic properties are major goals for corn breeders in northern Europe. In order to facilitate field grain yield determination we measured corn grain moisture content with near infrared (NIR) spectroscopy directly on a harvesting machine. NIR spectroscopy, in combination with harvesting, significantly improved quality and speed of yield determination within the very narrow harvest time window. Moisture calibrations were developed with 2117 samples from the 2001 to 2003 crop seasons using six diode array spectrometers mounted on combines. These models were derived from databases containing spectra from all instruments. Spectrometer-specific calibrations cannot be used to predict samples measured on other instruments of the same type. Standard error of cross-validation ( SECV) and coefficient of determination ( R2) were 0.56 and 0.99%, respectively. Moisture standard errors of prediction ( SEPs) for the six instruments, using varying independent sample sets from the 2004 harvest, ranged between 0.59% and 0.99% with R2 values between 0.92 to 0.98. The six instruments produced the same dry matter predictions on a common sample set as indicated by high R2 and low biases among them, hence there was no need to apply specific standardisation algorithms. Moisture NIR spectroscopy determinations were significantly more precise than those obtained using the reference method. Analysis of variance revealed low least significant differences and high heritabilities. High precision and heritability demonstrate successful implementation of on-combine NIR spectroscopy for routine dry matter (yield) measurements.

Near infrared spectroscopy for rapid determination of solids content of amino resins

2020 ◽  
Vol 28 (5-6) ◽  
pp. 344-350
Author(s):  
M Gonçalves ◽  
NT Paiva ◽  
JM Ferra ◽  
J Martins ◽  
F Magalhães ◽  
...  
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Water Addition ◽  
Reliable Technique ◽  
Standard Normal Variate ◽  
And Performance ◽  
Solids Content

Near infrared (NIR) spectroscopy is a fast and reliable technique for assessing properties of amino resins. One important property that defines the cost and performance of these resins is the solids content (SC). This work studied the prediction of SC of amino resins by combining NIR spectroscopy with partial least squares (PLS) regression. A total of 990 industrial NIR spectra of amino resins were obtained and split randomly by a ratio of 2/3 for calibration and 1/3 for validation. The best model achieved a root mean-square error of prediction (RMSEP) of 0.32% (m/m) and a coefficient of determination of prediction ([Formula: see text]) of 81%. standard normal variate (SNV) was found to be the NIR pre-processing that provided the best results for model construction. Addition of water to two amino resins showed that the NIR model does not respond to the water addition, despite water making great contribution to the SC value. An inference that can be obtained from this is that the NIR model of amino resins uses NIR properties of amino resins that relate to the SC and from there predict the most probable SC, instead of looking at all the components that affect the SC of amino resins.

Estimating mechanical properties and specific gravity for five-year-old Eucalyptus tereticornis having broad moisture content range by NIR spectroscopy

Holzforschung ◽  
2011 ◽  
Vol 65 (5) ◽  
Author(s):  
Vimal Kothiyal ◽  
Aasheesh Raturi
Keyword(s):  
Specific Gravity ◽  
Moisture Content ◽  
Near Infrared ◽  
Wood Property ◽  
Nir Spectroscopy ◽  
Modulus Of Rupture ◽  
Bending Test ◽  
Coefficient Of Determination ◽  
Eucalyptus Tereticornis ◽  
Least Squares Regression

Abstract Near infrared spectroscopy coupled with multivariate data analysis has been used to predict the specific gravity, modulus of rupture, modulus of elasticity, and fiber stress at elastic limit in bending tests on radial and tangential strip wood samples obtained from five-year-old Eucalyptus tereticornis. Moisture content of samples was 6–21% for bending test and 7–16% for specific gravity. Partial least squares regression calibrations were developed for each wood property. Calibrations had good relationships between values measured in laboratory and NIR predicted values obtained from small clear samples. The coefficient of determination (R2) for calibration ranged from 0.76 to 0.83 and for prediction (Rp 2) it was between 0.58 and 0.77. Both radial and tangential faces are equally suited for calibration (for radial face R2 was 0.77–0.83 and for tangential it was 0.76–0.83). Standard errors of predictions were slightly higher compared to standard error of calibration.

Prediction of internal compositions change in potato during storage using visible/near-infrared (Vis/NIR) spectroscopy

2020 ◽  
Vol 16 (4) ◽  
Author(s):  
Roya Farhadi ◽  
Amir H. Afkari-Sayyah ◽  
Bahareh Jamshidi ◽  
Ahmad Mousapour Gorji
Keyword(s):  
Moisture Content ◽  
Cold Storage ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Reducing Sugar ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Reliable Technique

AbstractVisible/Near-infrared (Vis/NIR) spectroscopy at a range of 450–1000 nm was used to predict the values of three qualitative variables (starch, reducing sugar, and moisture content) on 200 potato tubers from 2 potato genotypes (‘Agria’ and ‘Clone 397009–8’) stored in both traditional and cold storages. After spectroscopy measurements, these variables were measured using reference methods. Then, Partial Least Square (PLS) models were developed. To evaluate developed models, Root Mean Square Error of calibration and cross validation (RMSEC and RMSECV), as well as coefficient of determination for calibration and cross validation (R2C and R2CV), and Residual Predictive Deviation (RPD) were used. The best prediction belonged to reducing sugar with statistical values of R2C = 0.99, R2CV = 0.98, RMSEC = 0.029, RMSECV = 0.037, and RPD = 7.57 in ‘Clone’ genotype stored under cold storage. The weakest prediction was related to moisture content with statistical values of R2C = 0.93, R2CV = 0.92, RMSEC = 0.268, RMSECV = 0.279, and RPD = 6.45 in stored ‘Clone’ genotypes under cold storage. Results of the study showed that, Vis/NIR spectroscopy as a non-destructive, fast, and reliable technique can be used for prediction of inner compositions of stored potatoes.

Rapid detection of quality of Japanese fermented soy sauce using near-infrared spectroscopy

2020 ◽  
Vol 12 (18) ◽  
pp. 2347-2354 ◽  
Author(s):  
Shuo Wang ◽  
Takehiro Tamura ◽  
Nobuyuki Kyouno ◽  
Xiaofang Liu ◽  
Han Zhang ◽  
...  
Keyword(s):  
Quality Control ◽  
Infrared Spectroscopy ◽  
Rapid Detection ◽  
Near Infrared ◽  
Control Method ◽  
Nir Spectroscopy ◽  
Soy Sauce ◽  
Quality Control Method ◽  
Robust Model

The application of NIR spectroscopy has great potential as an alternative quality control method, which provides a robust model for routinely estimating the final quality of soy sauce production rapidly and economically.

scholarly journals Quality Degradation of Chinese White Lotus Seeds Caused by Dampening during Processing and Storage: Rapid and Nondestructive Discrimination Using Near-Infrared Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Lu Xu ◽  
Hai-Yan Fu ◽  
Chen-Bo Cai ◽  
Yuan-Bin She
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Standard Normal Variate ◽  
Chinese White ◽  
Standard Normal ◽  
Processing And Storage ◽  
Sensitivity Specificity ◽  
Spectral Ranges ◽  
And Storage

Dampening during processing or storage can largely influence the quality of white lotus seeds (WLS). This paper investigated the feasibility of using near-infrared (NIR) spectroscopy and chemometrics for rapid and nondestructive discrimination of the dampened WLS. Regular (n=167) and dampened (n=118) WLS objects were collected from five main producing areas and NIR reflectance spectra (4000–12000 cm−1) were measured for bare kernels. The influence of spectral preprocessing methods, including smoothing, taking second-order derivatives (D2), and standard normal variate (SNV), on partial least squares discrimination analysis (PLSDA) was compared to select the optimal data preprocessing method. A moving-window strategy was combined with PLSDA (MWPLSDA) to select the most informative wavelength intervals for classification. Based on the selected spectral ranges, the sensitivity, specificity, and accuracy were 0.927, 0.950, and 0.937 for SNV-MWPLSDA, respectively.

scholarly journals Determination of Moisture Content and Basic Density of Poplar Wood Chips under Various Moisture Conditions by Near-Infrared Spectroscopy

2019 ◽  
Vol 65 (5) ◽  
pp. 548-555 ◽  
Author(s):  
Long Liang ◽  
Guigan Fang ◽  
Yongjun Deng ◽  
Zhixin Xiong ◽  
Ting Wu
Keyword(s):  
Moisture Content ◽  
Near Infrared ◽  
Prediction Models ◽  
Basic Density ◽  
Free Water ◽  
Nir Spectroscopy ◽  
Wood Chips ◽  
Coefficient Of Determination ◽  
Poplar Wood ◽  
Absorption Bands

AbstractThe potential of near-infrared (NIR) spectroscopy coupled with partial least-squares (PLS) regression was used to determine the moisture content and basic density of poplar wood chips. NIR spectra collected from the surface of wood chips were used to develop calibration models for moisture content and basic density predication, and various spectral preprocessing techniques were applied to improve the accuracy and robustness of the prediction models. The models were tested using totally independent sample sets and exhibited acceptable predictive performance for moisture content (coefficient of determination for prediction [R2p] = 0.98 and standard error of prediction [SEP] = 2.51 percent) and basic density (R2p = 0.87 and SEP = 17.61 kg m–3). In addition, the effect of moisture variations on prediction of basic density was investigated based on NIR spectra from wood chips under various moisture levels. The results demonstrated that broad absorption bands from water molecules, especially when free water exists in the cell lumen, overlap with informative signals related to wood properties and weaken the calibration relation between spectral features and basic density. Thus, maintaining wood chips in a low and even moisture state would help achieve reliable estimates of wood density by NIR analysis models.

Effects of Grain Moisture Content on Mechanical Grain Harvesting Quality of Summer Maize

2018 ◽  
Vol 44 (12) ◽  
pp. 1747 ◽  
Author(s):  
Lu-Lu LI ◽  
Jun XUE ◽  
Rui-Zhi XIE ◽  
Ke-Ru WANG ◽  
Bo MING ◽  
...  
Keyword(s):  
Moisture Content ◽  
Summer Maize ◽  
Grain Moisture

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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