scholarly journals Quantitative Identification of Adulterated Sichuan Pepper Powder by Near-Infrared Spectroscopy Coupled with Chemometrics

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xi-Yu Wu ◽  
Shi-Ping Zhu ◽  
Hua Huang ◽  
Dan Xu
Keyword(s):  
Rice Bran ◽  
Wheat Bran ◽  
Near Infrared ◽  
Direct Detection ◽  
Nir Spectroscopy ◽  
Corn Flour ◽  
Pls Analysis ◽  
Quantitative Identification ◽  
Pepper Powder ◽  
Direct Detection Method

Sichuan pepper is a traditional and important flavoring of Chinese cuisine. It has attracted increasing interest in recent years owning to its unique taste and aroma. However, some cheap adulterants have been illegally found in Sichuan pepper powder in the market due to merchants trying to cut costs and gain an extra profit. In order to determine the compositions of Sichuan pepper powder quickly and effectively, a direct detection method using near-infrared (NIR) spectroscopy has been developed. 462 samples of adulterated Sichuan pepper powder mixed with different amounts of wheat bran, rice bran, corn flour, and rosin powder were studied. The NIR spectra data was studied using partial least squares (PLS) analysis. The method was found to be capable of predicting the compositions of adulterated Sichuan pepper powder. The determination coefficients of prediction set (Rp2) with the best pretreatments were 0.971 for Sichuan pepper powder, 0.948 for rice bran, 0.969 for wheat bran, 0.967 for corn flour, and 0.994 for rosin powder, respectively. The standard errors of prediction (SEP) were 2.81%, 2.38%, 3.19%, 2.46%, and 1.10%, respectively. The results showed that NIR spectroscopy with chemometrics is a rapid and nondestructive tool for the quantitative analysis of adulterated Sichuan pepper powder.

scholarly journals Application of NIR spectroscopy in gluten detection as a cross-contaminant in food

2018 ◽  
Vol 13 (3-4) ◽  
pp. 120-127 ◽  
Author(s):  
Marija Radman ◽  
Tamara Jurina ◽  
Maja Benković ◽  
Ana Jurinjak Tušek ◽  
Davor Valinger ◽  
...  
Keyword(s):  
Wheat Flour ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Rice Flour ◽  
Good Prediction ◽  
Cross Contamination ◽  
Corn Flour ◽  
Food Ingredient ◽  
Special Groups

The determination of gluten is of critical importance when food screening is intended for special groups such as food ingredient intolerant and allergic persons. Cross-contamination of food that does not contain gluten is also possible in the sales chain. The aim of this study was to determine the applicability of Near-Infrared Spectroscopy (NIRs) for the detection of gluten traces in rice, rice flour, corn flour and corn grits. In the cross-contamination simulation, two types of wheat flour were used. They were added to rice, rice flour, corn flour and corn grits in a range from 5 % to 30 %. Apart from the spectra of pure and contaminated samples, conductivity and total dissolved solids were monitored to determine changes in the samples. NIR spectroscopy was combined with chemometric techniques to determine at which wavelengths a glutenfree fingerprint can be detected. Although experiments were carried out with a NIR instrument that monitors molecular vibrations in the range of λ= 904-1699 nm, the gluten fingerprint was successfully determined, regardless of the type of flour that was added to the rice, rice flour, corn flour and corn grits. All concentrations of the added flours were successfully determined and models were developed to detect the concentrations of the added flours. Even the conductivity showed good prediction potential in gluten determination. Regardless if the investigated samples were contaminated or not, the determination coefficient R2 was over 0.9. Developed models could be used to predict possible wheat flour contamination of any rice or corn product samples or samples prepared for cooking in water.

scholarly journals Estimation of Acacia melanoxylon unbleached Kraft pulp brightness by NIR spectroscopy

2015 ◽  
Vol 24 (2) ◽  
pp. eRC03 ◽  
Author(s):  
António J.A. Santos ◽  
Ofélia Anjos ◽  
Helena Pereira
Keyword(s):  
Kraft Pulp ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Present Model ◽  
Nir Spectroscopy ◽  
Kraft Pulps ◽  
Least Squares Regression ◽  
Acacia Melanoxylon ◽  
Pls Analysis ◽  
Plsr Model

<p><em>Aim of the study:</em> The ability of NIR spectroscopy for predicting the ISO brightness was studied on unbleached Kraft pulps of <em>Acacia melanoxylon</em> R. Br.</p><p><em>Area of study: </em>Sites covering littoral north, mid interior north and centre interior of Portugal.</p><p><em>Materials and methods:</em> The samples were Kraft pulped in standard identical conditions targeted to a kappa number of 15. A Near Infrared (NIR) partial least squares regression (PLSR) model was developed for the ISO brightness prediction using 75 pulp samples with a variation range of 18.9 to 47.9 %.</p><p><em>Main results:</em> Very good correlations between NIR spectra and ISO brightness were obtained. Ten methods were used for PLS analysis (cross validation with 48 samples), and a test set validation was made with 27 samples. The 1stDer pre-processed spectra coupling two wavenumber ranges from 9404 to 7498 cm<sup>-1</sup> and 4605 to 4243 cm<sup>-1</sup> allowed the best model with a root mean square error of ISO brightness prediction of 0.5 % (RMSEP), a r<sup>2</sup> of 99.5 % with a RPD of 14.7.</p><p><em>Research highlights:</em> According to AACC Method 39-00, the present model is sufficiently accurate to be used for process control (RPD ≥ 8).</p><p class="BioresourcesKeywords"><strong>Key words:</strong>  Acacia melanoxylon;<em> unbleached Kraft pulps; ISO Brightness; NIR; RPD.</em></p>

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.

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