Prediction of beef quality traits through mini NIR spectrophotometer and multivariate analyses

The aim of this study was to test the ability of mini NIR reflectance spectroscopy to predict beef quality traits. Sixty M. longissimus thoracis were collected and spectra were obtained prior to beef quality trait analysis. Calibration equations were developed from reference data (n=60) of pH, color traits (lightness, redness and yellowness), drip loss (%), cooking loss (%), CP (%), EE (%), moisture (%), DM (%), and Ash (%) using partial least squares regressions. Predictive ability of the models was assessed by coefficient of determination of cross-validation (R2CV) and root mean square error of cross-validation. Predictions models were satisfactory (R2CV = 0.95) for pH, (R2CV = 0.96) for lightness (L*), (R2CV = 0.96) for redness (a*), (R2CV = 0.97) for yellowness (b*), (R2CV = 0.95) for drip loss, (R2CV = 0.95) for cooking loss, (R2CV = 0.94) for CP, (R2CV = 0.95) for EE, (R2CV = 0.91) for moisture, (R2CV = 0.91) for DM and (R2CV = 0.91) for ash. The ratio performance deviation is 5.35, 5.34, 5.87, 5.16, 4.64, 4.81, 4.45, 4.95, 3.36, 4.73 and 4.47 for L*, a*, b*, pH, drip loss, cooking loss, CP, EE, moisture, DM and Ash respectively which indicates that all values are adequate for analytical purposes. Range error ratio are 20.69, 22.97, 27.11, 18.92, 20.74, 16.20, 17.80, 17.52, 14.96, 17.89 and 17.87 for L*, a*, b*, pH, drip loss, cooking loss, CP, EE, moisture, DM and ash respectively. From the findings of this study it can be concluded that mini NIRS is a suitable tool for a rapid, non-destructive and reliable prediction of beef quality.

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration

The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (RPcv2) and the root mean square error (RMSEPcv) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the RPcv2 and RMSEPcv were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils.

Optimized Method for Mapping Inorganic Pigments by Means of Multispectral Imaging Combined with Hyperspectral Spectroscopy for the Study of Vincenzo Pasqualoni’s Wall Painting at the Basilica of S. Nicola in Carcere in Rome

Multispectral imaging is a preliminary screening technique for the study of paintings. Although it permits the identification of several mineral pigments by their spectral behavior, it is considered less performing concerning hyperspectral imaging, since a limited number of wavelengths are selected. In this work, we propose an optimized method to map the distribution of the mineral pigments used by Vincenzo Pasqualoni for his wall painting placed at the Basilica of S. Nicola in Carcere in Rome, combining UV/VIS/NIR reflectance spectroscopy and multispectral imaging. The first method (UV/VIS/NIR reflectance spectroscopy) allowed us to characterize pigment layers with a high spectral resolution; the second method (UV/VIS/NIR multispectral imaging) permitted the evaluation of the pigment distribution by utilizing a restricted number of wavelengths. Combining the results obtained from both devices was possible to obtain a distribution map of a pictorial layer with a high accuracy level of pigment recognition. The method involved the joint use of point-by-point hyperspectral spectroscopy and Principal Component Analysis (PCA) to identify the pigments in the color palette and evaluate the possibility to discriminate all the pigments recognized, using a minor number of wavelengths acquired through the multispectral imaging system. Finally, the distribution and the spectral difference of the different pigments recognized in the multispectral images, (in this case: red ochre, yellow ochre, orpiment, cobalt blue-based pigments, ultramarine and chrome green) were shown through PCA false-color images.