scholarly journals Estimating Fat Components of Potato Chips Using Visible and Near-Infrared Spectroscopy and a Compositional Calibration Model

2021 ◽  
Author(s):  
Javier Palarea-Albaladejo ◽  
José Antonio Cayuela-Sánchez ◽  
Elena Moriana-Correro
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Compositional Data ◽  
Statistical Treatment ◽  
Potato Chips ◽  
Calibration Model ◽  
Ongoing Research ◽  
Nutritional Label ◽  
Fat Composition

AbstractWhen aiming to assess the fat composition of commercial potato chip products, their diversity and the difficulties to verify the nutritional label of batches of chips by official methods are main challenges. Thus, the possibility of using alternative technologies is of great interest for both the industry and the public administration. Near-infrared spectroscopy (NIRS) is a rapid and non-destructive technique that has been proven useful in different applications in the food industry. However, suitable specific treatments of compositional references with NIRS methods have been until now very scarce in the literature. The nutritional label information is commonly given as percentage content values across several nutritional categories. This formally corresponds with the class of so-called compositional data, for which there are specific statistical methods. This study contributes to ongoing research on the feasibility of Vis/NIR spectroscopy for food nutritional labelling. In particular, a calibration model is formulated to estimate the relative content of fat in potato chips products based on NIR spectral signal that integrates a consistent statistical treatment of the nutritional reference data. The method provides accurate estimates of the fat composition, with this including saturated, monounsaturated, and polyunsaturated types of fat, as well as their total fat percentage (cross-validated overall R2 = 0.88 and R2 = 0.82 from ground and fragmented samples respectively) and shows its potential for both nutritional labelling and verification in a rapid and inexpensive manner.

scholarly journals Determination of glomalin in agriculture and forest soils by near-infrared spectroscopy

2017 ◽  
Vol 63 (No. 5) ◽  
pp. 226-230 ◽  
Author(s):  
Zbíral Jiří ◽  
Čižmár David ◽  
Malý Stanislav ◽  
Obdržálková Elena
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Near Infrared ◽  
Arable Land ◽  
Calibration Model ◽  
Sustainable Land Management ◽  
Chemical Methods ◽  
Calibration Models

Determining and characterizing soil organic matter (SOM) cheaply and reliably can help to support decisions concerning sustainable land management and climate policy. Glomalin was recommended as one of possible indicators of SOM quality. Extracting glomalin from and determining it in soils using classical chemical methods is too complicated and therefore near-infrared spectroscopy (NIRS) was studied as a method of choice for the determination of glomalin. Representative sets of 84 different soil samples from arable land and grasslands and 75 forest soils were used to develop NIRS calibration models. The parameters of the NIRS calibration model (R = 0.90 for soils from arable land and grasslands and R = 0.94 for forest soils) proved that glomalin can be determined in air-dried soils by NIRS with adequate trueness and precision simultaneously with determination of nitrogen and oxidizable carbon.

scholarly journals Estimation of selected properties of forest soils using near-infrared spectroscopy (NIR)

2016 ◽  
Vol 67 (1) ◽  
pp. 32-36 ◽  
Author(s):  
Mateusz Kania ◽  
Piotr Gruba
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Forest Soils ◽  
Near Infrared ◽  
C:N Ratio ◽  
Soil Samples ◽  
Initial Assessment ◽  
Calibration Model ◽  
Organic Carbon Content ◽  
Spectra Analysis

Abstract The study was focused on the application of near-infrared spectroscopy (NIR) as a tool for evaluation of selected properties of forest soils. We analysed 144 soil samples from the topsoil of nine plots located in southern Poland. Six plots were established under pine stands, and three plots under oak stands. The NIR measurements were performed using Antharis II FT scanner. On the basis of the spectrum files obtained from scanning of 96 samples and the measurement results obtained for selected properties of the soil samples, we developed a calibration model. The model was validated using 48 independent samples. We attempted to estimate the following properties of forest soils: pH, C:N ratio, the organic carbon content (Ct), total nitrogen (Nt), clay content (Clay), base cation content (BC), cation exchange capacity (CEC) and total acidity (TA). We conclude that estimation of soil properties using NIR method can be applied as additional (to laboratory analysis) or initial assessment of soil quality. Our results also suggest that forest species composition may affect the mathematical model applied to NIR spectra analysis, however, this hypothesis needs some of further investigations.

Near infrared spectroscopy on ears to classify pigs based on carcass and fat composition

Meat Science ◽  
2016 ◽  
Vol 112 ◽  
pp. 183 ◽  
Author(s):  
N. Prieto ◽  
M. Juárez ◽  
M.E.R. Dugan ◽  
R.T. Zijlstra ◽  
J.L. Aalhus
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fat Composition

Fast Determination of Active Components in Fructus Corni by Near Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 2054-2058
Author(s):  
Hai Yan Gong ◽  
Ya Nan Hu ◽  
Cai Xia Xie ◽  
Yong Xia Cui ◽  
Yan Bai
Keyword(s):  
Quality Control ◽  
Infrared Spectroscopy ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Calibration Model ◽  
Mean Square ◽  
Active Components ◽  
Near Infrared Reflectance ◽  
Pharmaceutical Industries

Today, near-infrared (NIR) has been proved to be a powerful analytical tool. It has been applied widely in agricultural, petrochemical, textile and pharmaceutical industries. In this paper, near-infrared spectroscopy (NIRS) combined with partical least square (PLS) was used as a qualitative tool to rapidly determinate two active components in Fructus Corni. The PLS calibration model of NIR Spectroscopy, the correlation coefficients (R2) of Loganin and Morroniside were 0.95895 and 0.98450, the root-mean-square error of cross-validation (RMSECV), the Correction of deviation, the prediction mean square error was 0.0344,0.109;0.0625, 0.2641 and 0.0948, 0.233. The result shows that, the near-infrared Reflectance Spectroscopy could be used to determinate the content of Loganin and Morroniside, and meanwhile as a simple and rapid new method for the quality assessment of Fructus Corni. In addition, the NIRS has a unique advantage in the quality control of traditional Chinese Medicine (TCM), such as rapid, accurate, nondestructive and no pollution. It is expected to be further uses in the quality control of TCM. It is can achieve the requirement of rapid detection of large quantities of Fructus Corni.

Rapid determination of oil content in dried-ground oil palm mesocarp and kernel using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 338-347 ◽  
Author(s):  
Sudarno ◽  
Divo D Silalahi ◽  
Tauvik Risman ◽  
Baiq L Widyastuti ◽  
F Davrieux ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Oil Palm ◽  
Standard Error ◽  
Oil Content ◽  
Near Infrared ◽  
Cross Validation ◽  
Calibration Model ◽  
Reliable Technique

Near infrared spectroscopy calibrations for rapid oil content determination of dried-ground oil palm mesocarp and kernel were developed. Samples were analyzed, one set using the Soxhlet extraction method for reference analysis and the other set scanned by near infrared spectroscopy instrument for calibration. Successful calibrations were obtained with good accuracy and precision for mesocarp and kernel, based on statistical models. Math treatment and scatter correction had significant effects on the fitting of the calibration model. The best obtained calibration models were demonstrated by multiple correlation coefficient (R2), standard error of calibration, standard error of cross validation, coefficient of determination in cross validation (1-VR) and relative predictive deviation of calibration, which respectively were 0.997, 1.21%, 1.23%, 0.997 and 17.89 for mesocarp and 0.952, 0.47%, 0.53%, 0.94 and 4.00 for kernel. The correlations between reference and predicted values for samples in the validation sets were in agreement with high linearity, high ratio performance to deviation of prediction (≥4.00) and low standard error of prediction samples for both samples. The results demonstrated that near infrared spectroscopy can be used as an alternative and reliable technique to estimate the mesocarp and kernel oil contents in dry matter basis accurately and rapidly.

scholarly journals Detection of deltamethrin in cabbages using visible shortwave near-infrared spectroscopy

Food Research ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 273-280
Author(s):  
C.D.M. Ishkandar ◽  
N.M. Nawi ◽  
R. Janius ◽  
N. Mazlan ◽  
T.T. Lin
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Spectral Data ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Calibration Model ◽  
Mean Square

Pesticides have long been used in the cabbage industry to control pest infestation. This study investigated the potential application of low-cost and portable visible shortwave near-infrared spectroscopy for the detection of deltamethrin residue in cabbages. A total of sixty organic cabbage samples were used. The sample was divided into four batches, three batches were sprayed with deltamethrin pesticide whereas the remaining batch was not sprayed (control sample). The first three batches of the cabbages were sprayed with the pesticide at three different concentrations, namely low, medium and high with the values of 0.08, 0.11 and 0.14% volume/volume (v/v), respectively. Spectral data of the cabbage samples were collected using visible shortwave near-infrared (VSNIR) spectrometer with wavelengths range between 200 and 1100 nm. Gas chromatography-electron capture detector (GC-ECD) was used to determine the concentration of deltamethrin residues in the cabbages. Partial least square (PLS) regression method was adopted to investigate the relationship between the spectral data and deltamethrin concentration values. The calibration model produced the values of coefficient of determination (R2 ) and the root mean square error of calibration (RMSEC) of 0.98 and 0.02, respectively. For the prediction model, the values of R2 and the root mean square error of prediction (RMSEP) were 0.94 and 0.04, respectively. These results demonstrated that the proposed spectroscopic measurement is a promising technique for the detection of pesticide at different concentrations in cabbage samples.

scholarly journals Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8222
Author(s):  
Olga Escuredo ◽  
Laura Meno ◽  
María Shantal Rodríguez-Flores ◽  
Maria Carmen Seijo
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dry Matter ◽  
Near Infrared ◽  
Reducing Sugars ◽  
Quality Parameters ◽  
Portable Devices ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Potato Quality

The aim of the present work was to determine the main quality parameters on tuber potato using a portable near-infrared spectroscopy device (MicroNIR). Potato tubers protected by the Protected Geographical Indication (PGI “Patata de Galicia”, Spain) were analyzed both using chemical methods of reference and also using the NIR methodology for the determination of important parameters for tuber commercialization, such as dry matter and reducing sugars. MicroNIR technology allows for the attainment/estimation of dry matter and reducing sugars in the warehouses by directly measuring the tubers without a chemical treatment and destruction of samples. The principal component analysis and modified partial least squares regression method were used to develop the NIR calibration model. The best determination coefficients obtained for dry matter and reducing sugars were of 0.72 and 0.55, respectively, and with acceptable standard errors of cross-validation. Near-infrared spectroscopy was established as an effective tool to obtain prediction equations of these potato quality parameters. At the same time, the efficiency of portable devices for taking instantaneous measurements of crucial quality parameters is useful for potato processors.

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