Application of near-infrared reflectance spectroscopy in the determination of major components in taramosalata

Abstract

This work evaluates the feasibility of near-infrared (NIR) for determining moisture, protein and fat content of the famous Greek dish taramosalata. Furthermore, it is investigated the relative performance of the calibration model in the context of varying calibration data size and number of used wavelengths. Various calibration data sizes (n=40,50,…,80) and number of wavelengths (1, 2,…,6) were considered. Calibration samples were analyzed employing traditional chemical methods and scanned using an Instalab 600-Dickey-John NIR apparatus. Calibrations were achieved with the use of multilinear regression between chemical and spectral data from each calibration data set.

The optimal prediction errors (root mean square error of prediction, RMSEP), obtained with models based on six wavelengths and a calibration data size of 80, were 0.115% for moisture, 0.023% for protein and 0.088% for fat. The prediction errors decrease with an increase in calibration size and in number of wavelengths but seem to become stabilized around calibration data size 60 and number of wavelengths 3. Since a calibration set may be expensive to acquire and calibrations using many wavelengths tend to over-fit the data, the accurate calibrations based on three wavelengths and a calibration data size of 60 might be preferred. The RMSEP values obtained with these models were 0.271% for moisture, 0.115% for protein and 0.222% for fat.

NIR measurement as performed by the Dickey John Analyzer was proved a rapid and accurate method for analysis of taramosalata and may be used as a replacement for conventional expensive and time-consuming wet chemistry methods.

Introduction

With the great increase in food manufacture and trade, a large number of analyses of both raw materials and finished products are in demand. The classical analytical methods are slow, expensive and need highly qualified staff.

Near-infrared (NIR) spectroscopy represents a very rapid and accurate method for the simultaneous measurement of different constituents in various food and agricultural products. The NIR technique was first developed in the late 1930s and continues to be used. Current applications in food processing include the quantitative determinations of moisture, fat, oil, sugars, and protein in a wide range of products such as ground meat, snack foods, process cheese, flour, coffee, orange juice, and milk powder (Osborne, Fearn, & Hindle, 1993). The main advantages of NIR spectroscopy lie in its speed, the avoidance of the use of chemicals, the multiplicity of analyses from one spectrum, and no or little sample pretreatment (Hiukka, 1998; Laporte & Paquin, 1999). Furthermore, NIR technology may be an efficient tool for real time control of production lines (Adamopoulos, Goula, & Petropakis, 2001; Singh, Bhamidipati, Singh, Smith, & Nelson, 1996).

NIR reflectance instruments have an infrared light source, which is collimated and filtered into specific wavelengths. The filtered beam is directed onto the surface of the food material and the amount of energy reflected at several frequencies is measured and transformed to concentration data using a calibration stored in the central processor of the instrument. Selection of the “optimum” subset of wavelengths for NIR calibrations has been widely investigated (Huang et al., 2002). However, for a given data set, whether the relative performance of the calibration depends on calibration data size is unclear and commonly ignored.

Taramosalata is a famous Greek dish. It is a pastelike product prepared from taramas, which is ideally the dried and salted roe of the grey mullet, but more usually from the less expensive dried and salted cod or tuna roe. Moisture, protein and fat are the principal constituents of taramosalata, which, aside from determining its nutritious and energetic value, influence some of its physical characteristics. The determination of these constituents is the most important control in taramosalata production, but reference analyses for moisture, protein and fat are expensive and time-consuming.

Since studies on taramosalata analysis with NIR instruments have not been found in the literature, the objective of this study was to evaluate the feasibility of NIR for determining moisture, protein and fat content of taramosalata. Furthermore, it is investigated the relative performance of the calibration model in the context of varying calibration data size and number of used wavelengths. Various calibration data sizes and number of wavelengths were used in order to see whether relative performance of NIR calibration depends on these factors.