Determination of origin and sugars of citrus fruits using genetic algorithm, correspondence analysis and partial least square combined with fiber optic NIR spectroscopy.
The capacity to confirm the variety or origin and the estimation of sucrose, glucose, fructose of the citrus fruits are major interests of citrus juice industry. A rapid classification and quantification technique was developed and validated for simultaneous and nondestructive quantifying the sugar constituent's concentrations and the origin of citrus fruits using Fourier Transform Near-Infrared (FT-NIR) spectroscopy in conjunction with Artificial Neural Network (ANN) using genetic algorithm, Chemometrics and Correspondences Analysis (CA). To acquire good classification accuracy and to present a wide range of concentration of sucrose, glucose and fructose, we have collected 22 different varieties of citrus fruits from the market during the entire season of citruses. FT-NIR spectra were recorded in the NIR region from 1,100 to 2,500 nm using the fiber optic probe and three types of data analysis were performed. Chemometrics analysis using Partial Least Squares (PLS) was performed in order to determine the concentration of individual sugars. Artificial Neural Network analysis was performed for classification, origin or variety identification of citrus fruits using genetic algorithm. Correspondence analysis was performed in order to visualize the relationship between the citrus fruits. To compute a PLS model based upon the reference values and to validate the developed method, high performance liquid chromatography (HPLC) was performed. Spectral range and the number of PLS factors were optimized for the lowest standard error of calibration (SEC), prediction (SEP) and correlation coefficient (R(2)). The calibration model developed was able to assess the sucrose, glucose and fructose contents in unknown citrus fruit up to an R(2) value of 0.996-0.998. Numbers of factors from F1 to F10 were optimized for correspondence analysis for relationship visualization of citrus fruits based on the output values of genetic algorithm. ANN and CA analysis showed excellent classification of citrus according to the variety to which they belong and well-classified citrus according to their origin. The technique has potential in rapid determination of sugars content and to identify different varieties and origins of citrus in citrus juice industry.
Tewari
Jagdish C
JC
Department of Fiber Science and Apparel Design, Cornell University, Ithaca, NY 14853, USA. jct48@cornell.edu
Dixit
Vivechana
V
Cho
Byoung-Kwan
BK
Malik
Kamal A
KA
D000465:Algorithms; D001628:Beverages / Q000032:analysis; D002241:Carbohydrates / Q000032:analysis*; D002851:Chromatography, High Pressure Liquid; D002957:Citrus / Q000737:chemistry* / Q000145:classification*; D005336:Fiber Optic Technology; D005524:Food Technology; D005583:Fourier Analysis; D005632:Fructose / Q000032:analysis; D005947:Glucose / Q000032:analysis; D016018:Least-Squares Analysis; D016571:Neural Networks, Computer; D013045:Species Specificity; D019265:Spectroscopy, Near-Infrared; D013395:Sucrose / Q000032:analysis
D002241:Carbohydrates; D005632:Fructose; D013395:Sucrose; D005947:Glucose