Feasibility of using visible/near-infrared (Vis/NIR) spectroscopy to detect aflatoxigenic fungus and aflatoxin contamination on corn kernels

2018 ◽  
Author(s):  
Feifei Tao ◽  
Haibo Yao ◽  
Fengle Zhu ◽  
Zuzana Hruska ◽  
Yongliang Liu ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Aflatoxin Contamination ◽  
Corn Kernels

scholarly journals Use of Visible–Near-Infrared (Vis-NIR) Spectroscopy to Detect Aflatoxin B1 on Peanut Kernels

2019 ◽  
Vol 73 (4) ◽  
pp. 415-423 ◽  
Author(s):  
Feifei Tao ◽  
Haibo Yao ◽  
Zuzana Hruska ◽  
Yongliang Liu ◽  
Kanniah Rajasekaran ◽  
...  
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Spectroscopic Technique ◽  
Aflatoxin Contamination ◽  
Good Prediction ◽  
Peanut Kernel ◽  
Skilled Personnel ◽  
Food Commodities ◽  
Aflatoxin B

Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible–near-infrared (Vis-NIR) spectroscopy over the spectral range of 400–2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1 (AFB1). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB1-contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis-NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB1 contamination of peanut kernels.

Multi-Site Cr3+ Occupation Related Broadband NIR Luminescence in Cr3+-doped Li3Mg2NbO6

CrystEngComm ◽  
2021 ◽  
Author(s):  
Fen Xiao ◽  
Chengning Xie ◽  
Shikun Xie ◽  
Rongxi Yi ◽  
Huiling Yuan ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Luminescent Materials ◽  
Optical Source ◽  
Nir Emission ◽  
Nir Luminescence

Broadband near infrared (NIR) luminescent materials have attracted great attention recently for the advance smart optical source of NIR spectroscopy. In this work, a broadband NIR emission from 650 nm...

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.

Near infrared spectroscopy of Eucalyptus pellita for foliar nutrients and the potential for real-time monitoring of trees in fertiliser trial plots

2021 ◽  
pp. 096703352110079
Author(s):  
Agustan Alwi ◽  
Roger Meder ◽  
Yani Japarudin ◽  
Hazandy A Hamid ◽  
Ruzana Sanusi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Acacia Mangium ◽  
Nir Spectroscopy ◽  
Nutrient Status ◽  
Significant Loss ◽  
Eucalyptus Pellita ◽  
Operational Decision Making ◽  
Fertiliser Application ◽  
Planting Distance

Eucalyptus pellita F. Muell. has become an important tree species in the forest plantations of SE Asia, and in Malaysian Borneo in particular, to replace thousands of hectares of Acacia mangium Willd. which has suffered significant loss caused by Ceratocystis manginecans infection in Sabah, Malaysia. Since its first introduction at a commercial scale in 2012, E. pellita has been planted in many areas in the region. The species replacement requires new silvicultural practices to induce the adaptability of E. pellita to grow in the region and this includes relevant research to optimise such regimes as planting distance, pruning, weeding practices and nutrition regimes. In this present study, the nutritional status of the foliage was investigated with the aim to develop near infrared spectroscopic calibrations that can be used to monitor and quantify nutrient status, particularly total foliar nitrogen (N) and phosphorus (P) in the field. Spectra acquired on fresh foliage in situ on the tree could be used to predict N and P with accuracy suitable for operational decision-making regards fertiliser application. If greater accuracy is required, spectra acquired on dry, milled foliage could be used to predict N and P within a relative error of 10% (R2c, r2CV, RMSEP, RPD = 0.77, 0.71, 0.02 g 100/g, 1.9 for foliar P and = 0.90, 0.88, 0.21 g 100/g, 3.0 for foliar N on dry, milled foliage). The ultimate application of this is in situ nutrient monitoring, particularly to aid longitudinal studies in fertiliser trial plots and forest operations, as the non-destructive nature of NIR spectroscopy would enable regular monitoring of individual leaves over time without the need to destructively sample them. This would aid the temporal and spatial analysis of field data.

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

Recycling ◽  
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.

Evaluation of swelling properties and drug release from mechanochemical pre-gelatinized glutinous rice starch matrix tablets by near infrared spectroscopy

2021 ◽  
pp. 096703352098235
Author(s):  
Tomomi Takaku ◽  
Yusuke Hattori ◽  
Tetsuo Sasaki ◽  
Tomoaki Sakamoto ◽  
Makoto Otsuka
Keyword(s):  
Drug Release ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Matrix Tablets ◽  
Rice Starch ◽  
Release Time ◽  
Swelling Properties ◽  
X Ray ◽  
Glutinous Rice ◽  
Pharmaceutical Properties

The effect of grinding on the pharmaceutical properties of matrix tablets consisting of ground glutinous rice starch (GRS) and theophylline (TH) was predicted by near infrared (NIR) spectroscopy. Ground GRS samples were prepared by grinding GRS in a planetary ball mill for 0-120 min, measured by X-ray diffractometry (XRD) and NIR, and then evaluated for crystallinity (%XRD) based on XRD profiles. Tablets containing TH (5 w/w%), ground GRS (94 w/w%), and magnesium stearate (1 w/w%) were formed by compression. Gel-forming and drug-release processes of the tablets were measured using a dissolution instrument with X-ray computed tomography (XCT). Swelling ratio (SWE) and mean drug-release time (MDT) were evaluated based on XCT and drug-release profiles, respectively. Calibration models for predicting percent %XRD, MDT, and SWE were constructed based on the NIR of ground GRS using partial least-squares. The results indicated the possibility of controlling the pharmaceutical properties of matrix tablets by altering the pre-gelatinization of GRS based on changes in their NIR spectra during the milling process.

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