Yehia Badr

Abstract Tomato quality is one of the most important factors that helps ensuring a consistent marketing of tomato fruit. As ripeness is the main indicator for tomato quality from customers perspective, the determination of tomato ripeness stages is a basic industrial concern regarding tomato production in order to get high quality product. Automatic ripeness evaluation of tomato is an essential research topic as it may prove benefits in ensuring optimum yield of high quality product, this will increase the income because tomato is one of the most important crops in the world. This article presents an automated multi-class classification approach for tomato ripeness measurement and evaluation via investigating and classifying the different maturity/ripeness stages. The proposed approach uses color features for classifying tomato ripeness stages. The approach proposed in this article uses Principal Components Analysis (PCA) in addition to Support Vector Machines (SVMs) and Linear Discriminant Analysis (LDA) algorithms for feature extraction and classification, respectively. Experiments have been conducted on a dataset of total 250 images that has been used for both training and testing datasets with 10-fold cross validation. Experimental results showed that the proposed classification approach has obtained ripeness classification accuracy of 90.80%, using one-against-one (OAO) multi-class \{SVMs\} algorithm with linear kernel function, ripeness classification accuracy of 84.80% using one-against-all (OAA) multi-class \{SVMs\} algorithm with linear kernel function, and ripeness classification accuracy of 84% using \{LDA\} algorithm.

Abstract Five signal processing techniques were applied to ratio spectra for quantitative determination of bisoprolol (BIS) and hydrochlorothiazide (HCT) in their binary mixture. The proposed techniques are Numerical Differentiation of Ratio Spectra (ND-RS), Savitsky–Golay of Ratio Spectra (SG-RS), Continuous Wavelet Transform of Ratio Spectra (CWT-RS), Mean Centering of Ratio Spectra (MC-RS) and Discrete Fourier Transform of Ratio Spectra (DFT-RS). The linearity of the proposed methods was investigated in the range of 2–40 and 1–22 μg/mL for \{BIS\} and HCT, respectively. The proposed methods were applied successfully for the determination of the drugs in laboratory prepared mixtures and in commercial pharmaceutical preparations and standard deviation was less than 1.5. The five signal processing techniques were compared to each other and validated according to the İCH\} guidelines and accuracy, precision, repeatability and robustness were found to be within the acceptable limit.