Prediction of α-Solanine and α-Chaconine in Potato Tubers from Hunter Color Values and VIS/NIR Spectra

The glycoalkaloids contents of potato tubers are usually measured by the destructive analysis that consumes time and requires expensive high-performance equipment. This study was carried out to determine the possibility of nondestructive estimation of α-solanine and α-chaconine content in potato tubers. Visible/near-infrared (VIS/NIR) spectra, color values, and the reference α-solanine and α-chaconine were measured from 180 tubers of ‘Atlantic’ and ‘Trent’ potato cultivars with eight replications at two-week intervals during the storage up to ten weeks. The partial least square (PLS) regression method was used to develop models correlating color and spectra data to the measured reference data. Regression coefficient (r) between color variables (Hunter a ∗ , a ∗ / b ∗ , and ( a ∗ / b ∗ )2) and the actual measured values of a-solanine and a-chaconine content were 0.74, 0.62, and 0.62 and 0.70, 0.58, and 0.57, respectively, for the prediction set. Concurrently, equations were developed from color variables in multiple regression with r-values of 0.76 and 0.71 for α-solanine and α-chaconine, respectively. Additionally, the selected PLS model of VIS/NIR spectra had promising predictive power for α-solanine and α-chaconine with r-values of 0.68 and 0.63, respectively, between measured and predicted samples. Taken together, although it requires further studies to improve the prediction power of the developed models, the results of this study revealed the possibility of using VIS/NIR spectra and color variables for the prediction of α-solanine and α-chaconine contents from intact unpeeled potato tubers with chemical-free, fast, and cheap assessment methods.

CONTAMINATION DEPTH OF 137CS IN CONCRETE STRUCTURES

Uncertainty analysis for nondestructive estimation of contamination depth is presented. The contamination depth was determined using the peak-to-peak method as an in-situ measurement in which gamma spectra were measured by an HPGe detector. Since exponential activity distribution is a crucial assumption of this method, the distribution profile was confirmed by laboratory tests of core drill samples. The main parameter influencing uncertainty of contamination depth is uncertainty of relaxation length. The uncertainty is composed for statistical error represented by the ratio of net peak areas and systematic error given detection efficiency of measurement setup. Systematic relative error was evaluated to be 7.45%. Statistical relative error was evaluated to 9.97% for the proposed optimum net peak area. Variability of relaxation length was identified to be very low with mean value 2 mm with standard deviation 0.73 mm. For fixed relaxation length, it should be possible to estimate contamination depth by nonspectrometric devices.

Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network

Nondestructive estimation of the various physicochemical features of food such as fruits and vegetables will create a dramatic development in the food industry. The reason for this development is that the estimation is non-destructive, online, and most importantly fast. Regarding the advantages, various researchers have focused on how to undertake non-destructive estimation of the physicochemical features of various nutrients. Three main goals were pursued in this article. These are: 1. Nondestructive estimation of the chlorophyll b content of red delicious apple using color features and hybrid artificial neural network-cultural algorithm (ANN-CA), 2. Nondestructive estimation of chlorophyll b content of red delicious apple using spectral data (around a range of 680 nm) and hybrid Artificial Neural Network-biogeography-based algorithm (ANN-BBO), 3. Nondestructive estimation of the chlorophyll b content of red delicious apple using different groups of selective spectra by the hybrid artificial neural network-differential evolution algorithm (ANN-DA). In each of these methods, 1000 replications were performed to evaluate the reliability of various hybrids of the artificial neural network. Finally, the results indicated that the average determination coefficient in 1000 replications for the hybrid artificial neural network, the cultural algorithm, and the hybrid artificial neural network, the biogeography-based optimization algorithm, was 0.882 and 0.932, respectively. Also, the results showed that the highest value of the coefficient of determination among the different groups of effective features is related to the group of features with 10 spectra. The coefficient of determination in this case was 0.93.

Nondestructive estimation of remaining fatigue life without the loading history

A new nondestructive method to estimate the remaining fatigue life of a fatigue specimen with unknown knowledge of the loading history is presented. It requires only one short-time excitation test. The method utilizes the concept of damage parameter and the temperature rise to reliably predict the remaining number of cycles before fracture. A generalized procedure and numerous experimentally verified examples are presented. It is shown that the method can be applied to both constant and variable stress levels. Extensive laboratory tests reveal that the results of the remaining fatigue life predictions are in very good agreement with measurements.