scholarly journals Preliminary Assessment of Parmigiano Reggiano Authenticity by Handheld Raman Spectroscopy

Foods ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1563
Author(s):  
Mario Li Vigni ◽  
Caterina Durante ◽  
Sara Michelini ◽  
Marco Nocetti ◽  
Marina Cocchi
Keyword(s):  
Raman Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Calibration Model ◽  
Preliminary Assessment ◽  
Model Based ◽  
Intact Sample ◽  
Non Destructive ◽  
Maximum Content ◽  
Parmigiano Reggiano

Raman spectroscopy, and handheld spectrometers in particular, are gaining increasing attention in food quality control as a fast, portable, non-destructive technique. Furthermore, this technology also allows for measuring the intact sample through the packaging and, with respect to near infrared spectroscopy, it is not affected by the water content of the samples. In this work, we evaluate the potential of the methodology to model, by multivariate data analysis, the authenticity of Parmigiano Reggiano cheese, which is one of the most well-known and appreciated hard cheeses worldwide, with protected denomination of origin (PDO). On the other hand, it is also highly subject to counterfeiting. In particular, it is critical to assess the authenticity of grated cheese, to which, under strictly specified conditions, the PDO is extended. To this aim, it would be highly valuable to develop an authenticity model based on a fast, non-destructive technique. In this work, we present preliminary results obtained by a handheld Raman spectrometer and class-modeling (Soft Independent Modeling of Class Analogy, SIMCA), which are extremely promising, showing sensitivity and specificity of 100% for the test set. Moreover, another salient issue, namely the percentage of rind in grated cheese, was addressed by developing a multivariate calibration model based on Raman spectra. It was possible to obtain a prediction error around 5%, with 18% being the maximum content allowed by the production protocol.

scholarly journals The application of NIR spectroscopy in moisture determining of vegetable seeds

2020 ◽  
Vol 38 (No. 2) ◽  
pp. 131-136
Author(s):  
Wojciech Poćwiardowski ◽  
Joanna Szulc ◽  
Grażyna Gozdecka
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Moisture Analysis ◽  
Non Destructive

The aim of the study was to elaborate a universal calibration for the near infrared (NIR) spectrophotometer to determine the moisture of various kinds of vegetable seeds. The research was conducted on the seeds of 5 types of vegetables – carrot, parsley, lettuce, radish and beetroot. For the spectra correlation with moisture values, the method of partial least squares regression (PLS) was used. The resulting qualitative indicators of a calibration model (R = 0.9968, Q = 0.8904) confirmed an excellent fit of the obtained calibration to the experimental data. As a result of the study, the possibilities of creating a calibration model for NIR spectrophotometer for non-destructive moisture analysis of various kinds of vegetable seeds was confirmed.<br /><br />

scholarly journals Near-Infrared Spectroscopy Combined with Multivariate Calibration to Predict the Yield of Sesame Oil Produced by Traditional Aqueous Extraction Process

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yong-Dong Xu ◽  
Yan-Ping Zhou ◽  
Jing Chen
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Extraction Process ◽  
Sesame Oil ◽  
Aqueous Extraction ◽  
Oil Yield ◽  
Support Vector ◽  
Calibration Model ◽  
Aqueous Extraction Process

Sesame oil produced by the traditional aqueous extraction process (TAEP) has been recognized by its pleasant flavor and high nutrition value. This paper developed a rapid and nondestructive method to predict the sesame oil yield by TAEP using near-infrared (NIR) spectroscopy. A collection of 145 sesame seed samples was measured by NIR spectroscopy and the relationship between the TAEP oil yield and the spectra was modeled by least-squares support vector machine (LS-SVM). Smoothing, taking second derivatives (D2), and standard normal variate (SNV) transformation were performed to remove the unwanted variations in the raw spectra. The results indicated that D2-LS-SVM (4000–9000 cm−1) obtained the most accurate calibration model with root mean square error of prediction (RMSEP) of 1.15 (%, w/w). Moreover, the RMSEP was not significantly influenced by different initial values of LS-SVM parameters. The calibration model could be helpful to search for sesame seeds with higher TAEP oil yields.

scholarly journals Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

2005 ◽  
Vol 56 (4) ◽  
pp. 417 ◽  
Author(s):  
J. A. Guthrie ◽  
D. J. Reid ◽  
K. B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Updating ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Sample Selection ◽  
Model Performance ◽  
Soluble Solids ◽  
Internal Quality ◽  
Calibration Model ◽  
Validation Population

The robustness of multivariate calibration models, based on near infrared spectroscopy, for the assessment of total soluble solids (TSS) and dry matter (DM) of intact mandarin fruit (Citrus reticulata cv. Imperial) was assessed. TSS calibration model performance was validated in terms of prediction of populations of fruit not in the original population (different harvest days from a single tree, different harvest localities, different harvest seasons). Of these, calibration performance was most affected by validation across seasons (signal to noise statistic on root mean squared error of prediction of 3.8, compared with 20 and 13 for locality and harvest day, respectively). Procedures for sample selection from the validation population for addition to the calibration population (‘model updating’) were considered for both TSS and DM models. Random selection from the validation group worked as well as more sophisticated selection procedures, with approximately 20 samples required. Models that were developed using samples at a range of temperatures were robust in validation for TSS and DM.

scholarly journals Multivariate Method Based on Raman Spectroscopy for Quantification of Dipyrone in Oral Solutions

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Luciana Lopes Guimarães ◽  
Letícia Parada Moreira ◽  
Bárbara Faria Lourenço ◽  
Walber Toma ◽  
Renato Amaro Zângaro ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Quality Control ◽  
Principal Component Regression ◽  
Principal Component ◽  
Quantitative Model ◽  
Calibration Model ◽  
Analytical Methodology ◽  
Figures Of Merit ◽  
Model Based ◽  
Raman Probe

This work employed a quantitative model based on Raman spectroscopy and principal component regression (RS/PCR) to quantify the active ingredient dipyrone (metamizole) in commercially available formulations as an analytical methodology for quality control in the pharmaceutical industry. Raman spectra were collected using a dispersive Raman spectrometer (830 nm, 250 mW excitation, and 20 s exposure time) coupled to a Raman probe. Solutions of dipyrone diluted in water in the range of 80 to 120% of the concentration of commercial formulations (500 mg/mL) were used to develop a calibration model based on PCR to obtain the figures of merit for class I validation from the Brazilian Sanitary Surveillance Agency (ANVISA, RE no. 899/2003). This spectral model was then used to predict the concentration of dipyrone in commercial formulations from distinct brands with 500 mg/mL. A prediction error of 6.5 mg/mL (1.3%) was found for this PCR model using the diluted samples. Commercial formulations had predicted concentrations with a difference below 5.0% compared to the label concentration, indicating the applicability of Raman spectroscopy for quality control in the final product.

scholarly journals Implementation of Non Destructive FTNIR Method for Quick Estimation of Peanut Quality Based on FFA and Peroxide Value

2021 ◽  
Author(s):  
Rakesh Kumar Kumar Raigar ◽  
Shubhangi Srivast ◽  
Hari Niwas Mishra
Keyword(s):  
Peroxide Value ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Model Building ◽  
Nir Spectroscopy ◽  
Quality Parameters ◽  
Calibration Model ◽  
Peanut Kernel ◽  
Non Destructive

Abstract The possibility of rapid estimation of moisture, protein, fat, free fatty acid (FFA), and peroxide value (PV) content in peanut kernel was studied by Fourier transform near-infrared spectroscopy (FTNIR) in the diffuse reflectance mode along with chemometric technic. The moisture, fat and protein of fresh and damaged seeds of peanuts ranging from 3 to 9 %, 45 to 57 % and 23 to 27 % respectively, were used for the calibration model building based on partial least squares (PLS) regression. The peanut samples had major peaks at wavenumbers 53.0853, 4954.98, 4464.03, 4070.85, 74.75.63, 8230.21, and 6178.13 in per cm. First and second derivate mathematical preprocessing was also applied in order to eliminate multiple baselines for different chemical quality parameters of peanut. The FFA had the lowest value of calibration and validation errors (0.579 and 0.738) followed by the protein (0.736 and 0.765). The quality of peanut seeds with lowest root mean square error of cross validation of 0.76 and maximum correlation coefficient (R2) of 96.8 was obtained. The comprehensive results signify that FT-NIR spectroscopy can be used for rapid, non-destructive quantification of quality parameters in peanuts.

scholarly journals Real-Time Release Testing of Herbal Extract Powder by Near-Infrared Spectroscopy considering the Uncertainty around Specification Limits

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Guolin Shi ◽  
Bing Xu ◽  
Xin Wang ◽  
Zhong Xue ◽  
Xinyuan Shi ◽  
...  
Keyword(s):  
Product Quality ◽  
Real Time ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Process Analytical Technology ◽  
Herbal Extract ◽  
Calibration Model ◽  
Validation Data ◽  
Specification Limits ◽  
Release Testing

The concept of real-time release testing (RTRT) has recently been adopted by the production of pharmaceuticals in order to provide high-level guarantee of product quality. Process analytical technology (PAT) is an attractive and efficient way for realizing RTRT. In this paper, near-infrared (NIR) determination of cryptotanshinone and tanshinoneIIA content in tanshinone extract powders was taken as the research object. The aim of NIR analysis is to reliably declare the extract product as compliant with its specification limits or not. First, the NIR quantification method was developed and the parameters of the multivariate calibration model were optimized. The reliable concentration ranges covering the specification limits of two APIs were successfully verified by the accuracy profile (AP) methodology. Then, with the designed validation data from AP, the unreliability graph as the decision tool was built. Innovatively, the β-content, γ-confidence tolerance intervals (β-CTIs) around the specification limits were estimated. During routine use, the boundary of β-CTIs could help decide whether the NIR prediction results are acceptable. The proposed method quantified the analysis risk near the specification limits and confirmed that the unreliable region was useful to release the product quality in a real-time way. Such release strategy could be extended for other PAT applications to improve the reliability of results.

Sign in / Sign up

Export Citation Format

Share Document