scholarly journals Detection and Quantification of Tomato Paste Adulteration Using Conventional and Rapid Analytical Methods

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6059
Author(s):  
Flora Vitalis ◽  
John-Lewis Zinia Zaukuu ◽  
Zsanett Bodor ◽  
Balkis Aouadi ◽  
Géza Hitka ◽  
...  
Keyword(s):  
Analytical Methods ◽  
Near Infrared ◽  
Corn Starch ◽  
Nir Spectroscopy ◽  
Soluble Solid Content ◽  
Least Squares Regression ◽  
Food Ingredients ◽  
Linear Discriminant ◽  
Tomato Paste ◽  
Conventional Methods

Tomato, and its concentrate are important food ingredients with outstanding gastronomic and industrial importance due to their unique organoleptic, dietary, and compositional properties. Various forms of food adulteration are often suspected in the different tomato-based products causing major economic and sometimes even health problems for the farmers, food industry and consumers. Near infrared (NIR) spectroscopy and electronic tongue (e-tongue) have been lauded as advanced, high sensitivity techniques for quality control. The aim of the present research was to detect and predict relatively low concentration of adulterants, such as paprika seed and corn starch (0.5, 1, 2, 5, 10%), sucrose and salt (0.5, 1, 2, 5%), in tomato paste using conventional (soluble solid content, consistency) and advanced analytical techniques (NIR spectroscopy, e-tongue). The results obtained with the conventional methods were analyzed with univariate statistics (ANOVA), while the data obtained with advanced analytical methods were analyzed with multivariate methods (Principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares regression (PLSR). The conventional methods were only able to detect adulteration at higher concentrations (5–10%). For NIRS and e-tongue, good accuracies were obtained, even in identifying minimal adulterant concentrations (0.5%). Comparatively, NIR spectroscopy proved to be easier to implement and more accurate during our evaluations, when the adulterant contents were estimated with R2 above 0.96 and root mean square error (RMSE) below 1%.

scholarly journals Application of calibrations to hyperspectral images of food grains: example for wheat falling number

2017 ◽  
Author(s):  
Nicola Caporaso ◽  
Martin Whitworth ◽  
Ian Fisk
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Least Squares Regression ◽  
Falling Number ◽  
Linear Discriminant ◽  
Standard Normal Variate ◽  
Average Spectrum ◽  
Standard Normal ◽  
Improved Performance

The presence of a few kernels with sprouting problems in a batch of wheat can result in enzymatic activity sufficient to compromise flour functionality and bread quality. This is commonly assessed using the Hagberg Falling Number (HFN) method, which is a batch analysis. Hyperspectral imaging (HSI) can provide analysis at the single grain level with potential for improved performance. The present paper deals with the development and application of calibrations obtained using an HSI system working in the near infrared (NIR) region (~900–2500 nm) and reference measurements of HFN. A partial least squares regression calibration has been built using 425 wheat samples with a HFN range of 62–318 s, including field and laboratory pre-germinated samples placed under wet conditions. Two different approaches were tested to apply calibrations: i) application of the calibration to each pixel, followed by calculation of the average of the resulting values for each object (kernel); ii) calculation of the average spectrum for each object, followed by application of the calibration to the mean spectrum. The calibration performance achieved for HFN (R2 = 0.6; RMSEC ~ 50 sRMSEP ~ 63 s) compares favourably with other studies using NIR spectroscopy. Linear spectral pre-treatments lead to similar results when applying the two methods, while non-linear treatments such as standard normal variate showed obvious differences between these approaches. A classification model based on linear discriminant analysis (LDA) was also applied to segregate wheat kernels into low (<250 s) and high (>250 s) HFN groups. LDA correctly classified 86.4% of the samples, with a classification accuracy of 97.9% when using an HFN threshold of 150 s. These results are promising in terms of wheat quality assessment using a rapid and non-destructive technique which is able to analyse wheat properties on a single-kernel basis, and to classify samples as acceptable or unacceptable for flour production.

scholarly journals Detection of Monilia Contamination in Plum and Plum Juice with NIR Spectroscopy and Electronic Tongue

Chemosensors ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 355
Author(s):  
Flora Vitalis ◽  
David Tjandra Nugraha ◽  
Balkis Aouadi ◽  
Juan Pablo Aguinaga Bósquez ◽  
Zsanett Bodor ◽  
...  
Keyword(s):  
Near Infrared ◽  
Rapid Determination ◽  
Electronic Tongue ◽  
Nir Spectroscopy ◽  
Analytical Techniques ◽  
Brown Rot ◽  
Least Squares Regression ◽  
Linear Discriminant ◽  
Treatment Groups ◽  
Processed Form

Plums are one of the commercially important stone fruits that are available on the market in both fresh and processed form and the most sought-after products are prunes, cans, jams, and juices. Maturity, harvest, and post-harvest technologies fundamentally determine the relatively short shelf life of plums which is often threatened by Monilinia spp. Causing brown rot worldwide. The aim of the present research was to use advanced analytical techniques, such as hand-held near infrared spectroscopy (NIRS) and electronic tongue (e-tongue) to detect M. fructigena fungal infection on plums and quantify this fungal contamination in raw plum juices. For this purpose, plums were inoculated with fungal mycelia in different ways (control, intact, and through injury) and stored under different conditions (5 °C, and 24 °C) for eight days. The results obtained with the two instruments were analyzed with chemometric methods, such as linear discriminant analysis (LDA) and partial least squares regression (PLSR). The NIRS-based method proved successful when detectability before the appearance of visible signs of the infection was studied. E-tongue was able to detect and quantify the concentration of juice derived from plum developed with M. fructigena with RMSECV lower than 5% w/w. Overall, the two methods proved to be suitable for discriminating between the treatment groups, however, the classification accuracy was higher for samples stored at 24 °C. The research results show both NIRS and e-tongue are beneficial methods to reduce food waste by providing rapid determination of fruit quality.

scholarly journals Assessment of Wine Adulteration Using Near Infrared Spectroscopy and Laser Backscattering Imaging

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 95
Author(s):  
Anita Hencz ◽  
Lien Le Phuong Nguyen ◽  
László Baranyai ◽  
Donatella Albanese
Keyword(s):  
Prediction Error ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Laser Scattering ◽  
Added Sugar ◽  
Linear Discriminant ◽  
Negative Effect ◽  
Non Destructive ◽  
Water Dilution

Food adulteration is in the focus of research due to its negative effect on safety and nutritional value and because of the demand for the protection of brands and regional origins. Portugieser and Sauvignon Blanc wines were selected for experiments. Samples were made by water dilution, the addition of sugar and then a combination of both. Near infrared (NIR) spectra were acquired in the range of 900–1700 nm. Partial least squares regression was performed to predict the adulteration level. The model including all wines and adulterations achieved a prediction error of 0.59% added sugar and 6.85% water dilution. Low-power laser modules were used to collect diffuse reflectance signals at wavelengths of 532, 635, 780, 808, 850, 1064 nm. The general linear model resulted in a higher prediction error of 3.06% added sugar and 20.39% water dilution. Instead of classification, the present study investigated the feasibility of non-destructive methods in the prediction of adulteration level. Laser scattering successfully detected the added sugar with linear discriminant analysis (LDA), but its prediction accuracy was low. NIR spectroscopy might be suitable for rapid non-destructive estimation of wine adulteration.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

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

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;The study is performed in Mediterranean Croatia (44&amp;#176; 05&amp;#8217; N; 15&amp;#176; 22&amp;#8217; E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed &lt;em&gt;Quercus ssp.&lt;/em&gt; and &lt;em&gt;Juniperus ssp.&lt;/em&gt; forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22&lt;sup&gt;nd&lt;/sup&gt; 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 31&lt;sup&gt;st&lt;/sup&gt; and September 5&lt;sup&gt;th &lt;/sup&gt;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 (RMSE&lt;sub&gt;p&lt;/sub&gt;) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.&lt;/p&gt;&lt;p&gt;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 RMSE&lt;sub&gt;p &lt;/sub&gt;(Spectroscopy dataset RMSE&lt;sub&gt;p&lt;/sub&gt; = 1.91; Sentinel-2 dataset RMSE&lt;sub&gt;p&lt;/sub&gt; = 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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; remote sensing, soil spectroscopy, wildfires, soil organic matter&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgment: &lt;/strong&gt;This work was supported by the Croatian Science Foundation through the project &quot;Soil erosion and degradation in Croatia&quot; (UIP-2017-05-7834) (SEDCRO). Aleksandra Per&amp;#269;in is acknowledged for her cooperation during the laboratory work.&lt;/p&gt;

Ability of Vis-PIR spectroscopy to monitor changes in organic carbon of loamy soils at two depths

2021 ◽  
Author(s):  
Hayfa Zayani ◽  
Youssef Fouad ◽  
Didier Michot ◽  
Zeineb Kassouk ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Near Infrared ◽  
Full Range ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Calibration Data ◽  
Least Squares Regression ◽  
Data Set ◽  
Soil Carbon Stocks ◽  
The Mean

&lt;p&gt;Visible-Near Infrared (Vis-NIR) spectroscopy has proven its efficiency in predicting several soil properties such as soil organic carbon (SOC) content. In this preliminary study, we explored the ability of Vis-NIR to assess the temporal evolution of SOC content. Soil samples were collected in a watershed (ORE AgrHys), located in Brittany (Western France). Two sampling campaigns were carried out 5 years apart: in 2013, 198 soil samples were collected respectively at two depths (0-15 and 15-25 cm) over an area of 1200 ha including different land use and land cover; in 2018, 111 sampling points out of 198 of 2013 were selected and soil samples were collected from the same two depths. Whole samples were analyzed for their SOC content and were scanned for their reflectance spectrum. Spectral information was acquired from samples sieved at 2 mm fraction and oven dried at 40&amp;#176;C, 24h prior to spectra acquisition, with a full range Vis-NIR spectroradiometer ASD Fieldspec&amp;#174;3. Data set of 2013 was used to calibrate the SOC content prediction model by the mean of Partial Least Squares Regression (PLSR). Data set of 2018 was therefore used as test set. Our results showed that the variation &amp;#8710;SOC&lt;sub&gt;obs&lt;/sub&gt;&lt;sub&gt;&lt;/sub&gt;obtained from observed values in 2013 and 2018 (&amp;#8710;SOC&lt;sub&gt;obs&lt;/sub&gt; = Observed SOC (2018) - Observed SOC (2013)) is ranging from 0.1 to 25.9 g/kg. Moreover, our results showed that the prediction performance of the calibrated model was improved by including 11 spectra of 2018 in the 2013 calibration data set (R&amp;#178;= 0.87, RMSE = 5.1 g/kg and RPD = 1.92). Furthermore, the comparison of predicted and observed &amp;#8710;SOC between 2018 and 2013 showed that 69% of the variations were of the same sign, either positive or negative. For the remaining 31%, the variations were of opposite signs but concerned mainly samples for which &amp;#8710;SOCobs is less than 1,5 g/kg. These results reveal that Vis-NIR spectroscopy was potentially appropriate to detect variations of SOC content and are encouraging to further explore Vis-NIR spectroscopy to detect changes in soil carbon stocks.&lt;/p&gt;

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

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.

NIR Spectroscopy and Partial Least Squares Regression for the Determination of Phosphate Content and Viscosity Behaviour of Potato Starch

2001 ◽  
Vol 9 (2) ◽  
pp. 133-139 ◽  
Author(s):  
L.G. Thygesen ◽  
S.B. Engelsen ◽  
M.H. Madsen ◽  
O.B. Sørensen
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Potato Starch ◽  
Nir Spectroscopy ◽  
Peak Time ◽  
Phosphate Content ◽  
Least Squares Regression ◽  
Data Prediction

A set of 97 potato starch samples with a phosphate content corresponding to a phosphorus content between 0.029 and 0.11 g per 100 g dry matter was analysed using a Rapid Visco Analyzer (RVA) and near infrared (NIR) spectroscopy, (700–2498 nm). NIR-based prediction of phosphate content was possible with a root mean square error of cross-validation ( RMSECV) of 0.006% using PLSR (partial least squares regression). However, the NIR/PLSR model relied on weak spectral signals, and was highly sensitive to sample preparation. The best prediction of phosphate content from the RVA viscograms was a linear regression model based on the RVA variable Breakdown, which gave a RMSECV of 0.008%. NIR/PLSR prediction of the RVA variables Peak viscosity and Breakdown was successful, probably because they were highly related to phosphate content in the present data. Prediction of the other RVA variables from NIR/PLSR was mediocre (Through, Final Viscosity) or not possible (Setback, Peak time, Pasting temperature).

Application of near-infrared spectroscopy for the detection of internal insect infestation inPiceaabiesseed lots

2004 ◽  
Vol 34 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Mulualem Tigabu ◽  
Per Christer Odén ◽  
Tong Yun Shen
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Absorption Bands ◽  
Classification Rate ◽  
Data Set ◽  
Storage Lipids ◽  
Nir Absorption ◽  
Seed Origin ◽  
Single Seeds

The use of near-infrared (NIR) spectroscopy to discriminate between uninfested seeds of Picea abies (L.) Karst and seeds infested with Plemeliella abietina Seitn (Hymenoptera, Torymidae) larva is sensitive to seed origin and year of collection. Five seed lots collected during different years from Sweden, Finland, and Belarus were used in this study. Initially, seeds were classified as infested or uninfested with X-radiography, and then, NIR spectra from single seeds were collected with a NIR spectrometer from 1100 to 2498 nm with a resolution of 2 nm. Discriminant models were derived by partial least squares regression using raw and orthogonal signal corrected spectra (OSC). The resulting OSC model developed on a pooled data set was more robust than the raw model and resulted in 100% classification accuracy. Once irrelevant spectral variations were removed by using OSC pretreatment, single-lot calibration models resulted in similar classification rates for the new samples irrespective of origin and year of collection. Dis criminant analyses performed with selected NIR absorption bands also gave nearly 100% classification rate for new samples. The origin of spectral differences between infested and uninfested seeds was attributed to storage lipids and proteins that were completely depleted in the former by the feeding larva.

scholarly journals From the Laboratory to The Vineyard—Evolution of The Measurement of Grape Composition using NIR Spectroscopy towards High-Throughput Analysis

2019 ◽  
Vol 8 (4) ◽  
pp. 21
Author(s):  
Aoife Power ◽  
Vi Khanh Truong ◽  
James Chapman ◽  
Daniel Cozzolino
Keyword(s):  
Chemical Composition ◽  
Hyperspectral Imaging ◽  
High Throughput ◽  
Analytical Methods ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Chemical Compounds ◽  
Spectroscopic Techniques ◽  
High Throughput Analysis ◽  
Laboratory Methods

Compared to traditional laboratory methods, spectroscopic techniques (e.g., near infrared, hyperspectral imaging) provide analysts with an innovative and improved understanding of complex issues by determining several chemical compounds and metabolites at once, allowing for the collection of the sample “fingerprint”. These techniques have the potential to deliver high-throughput options for the analysis of the chemical composition of grapes in the laboratory, the vineyard and before or during harvest, to provide better insights of the chemistry, nutrition and physiology of grapes. Faster computers, the development of software and portable easy to use spectrophotometers and data analytical methods allow for the development of innovative applications of these techniques for the analyses of grape composition.

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