Determining nectarine cultivars using the spectroscopic method

Nectarine is a plant that is cultivated as an important commercial product in some countries and is known as an important source of sugar and vitamins in the human diet. Due to the increase in expectations for food products with high quality and safety standards, accurate, fast and targeted determination of the characteristics of food products is necessary. In the nectarine product, quality evaluation after the harvesting stage is necessary to provide a reliable and uniform product to the market. The purpose of this study is to identify and classify nectarines by extracting characteristics from the response patterns of the spectrometer and using chemometrics methods. A near-infrared spectrometer can detect the spectrum of reflected light by estimating its concentration or determining some of its inherent properties. The quality assessment of agricultural products includes two main methods, quality grading systems based on the external characteristics of agricultural products and quality grading systems based on internal quality assessment, which has gained outstanding points in recent years. In the meantime, several methods have been invented for the qualitative grading of agricultural products based on the assessment of their internal properties in a non-destructive manner, and only some of them have been able to meet the above conditions and have been justified in terms of technical and industrial aspects. To be meanwhile, spectrometry can be highly efficient in determining the quality of cultivars. Spectroscopy is a type of system that has a different structure and approach from other methods (image processing, neural network, etc.) and can perform classification and determination of digit quality. With increasing expectations for food products with high quality and safety standards, the need for accurate, fast and targeted determination of the characteristics of food products is now essential. Because manual methods do not have automatic control, they are very tiring, difficult and expensive, and they are easily affected by environmental factors. Today, spectroscopic systems are non-destructive and cost-effective and are ideally used for routine inspections and quality assurance in the food industry and related products. This technology allows inspection works to be carried out using wavelength data analysis techniques and is a non-destructive method for measuring quality parameters. In this research, using spectrometry and chemometrics methods, the variety of nectarine fruit was identified.

For this study, 5 different nectarine cultivars were prepared from the gardens of Moghan city (Ardebil province) and were tested and data collected. A spectroradiometer model PS-100 (Apogee Instruments, INC., Logan, UT, USA) was used to acquire the spectrum of the samples. This spectroradiometer is very small, light, and portable, has a single-wavelength sputtering type with a resolution of 1 nm and a linear silicon CCD array detector with 2048 pixels that covers the spectral range of 250-1150 nm (Vis/NIR) well. Also, there is the ability to connect the optical fibre to the PS-100 spectroradiometer and transfer the data to the computer with the purpose of displaying and storing the acquired spectra in the Spectra Wiz software through the USB port. With the aim of creating optimal light in contrast mode measurements, an OPTC (Halogen Light Source) model halogen-tungsten light source, which can be connected to an optical fibre, was used. This light source has three output powers of 10, 20, and 30 watts, which were used in this research. Also, a two-branch optical fibre probe model (Apogee Instruments, INC., Logan, Utah, USA), which includes 7 parallel optical fibres with a diameter of 400 micrometres, was used in counter-mode measurements. After providing the necessary equipment, the optimal spectroscopic arrangement was designed and implemented in order to facilitate the experiments and minimize the effect of environmental factors during the spectroscopic process. The data obtained from spectral imaging may be affected by the scattering of light by the detector with sample change, sample size change, surface roughness in the sample, the noise created due to the increase in temperature of the device and many other factors, and unwanted information affect the accuracy of calibration models. Therefore, to achieve stable, accurate and reliable calibration models, data pre-processing is needed (Rossel, 2008). In this research, Savitzky-Golay smoothing, first and second derivatives, baseline, standard normal distribution, and incremental scatter correction were applied to the data. The use of non-destructive methods based on spectroscopy in the full range of wavelengths requires spending time and very high costs, which makes the practical application of this method almost impossible; therefore, one should look for a way to find the optimal wavelengths and limit the wavelengths to the minimum possible value. Chemometrics uses multivariate statistics to extract useful information from complex analytical data. The chemometrics used in this study started with principal component analysis (PCA) to explore the output response of the sensors and reduce the dimensionality of the data. In the next step, linear diagnostic analysis (LDA) was also used to classify 5 varieties of Shail. (PCA) is one of the most common statistical data reduction methods. This method is an unsupervised technique used to explore and reduce the dimensionality of a dataset. The analysis itself involves the determination of variable components, which are linear combinations of many investigated characteristics. In this research, in order to construct the LDA model, the data were randomly divided into two parts: 70% of the samples were used for training and cross-validation, and the rest of the data were used for independent validation.

Based on the results of the PCA analysis presented in Figure 2, the first principal component (PC-1) describes 72% and the second principal component (PC-2) 13% of the variance of the tested samples. As a result, the first two principal components together express 85% of the data. Considering that it is possible that the degree of correlation between the properties of different samples during the tests, due to various reasons such as technical problems of the equipment, data collection, incorrect sampling, etc., in some samples, inappropriate or socalled outliers The LDA method is a supervised method that is used to find the most distinct eigenvectors and maximizes the between-class and intra-class variance ratios and is capable of classifying two or more groups of samples. The LDA method was used to identify the nectarine cultivars based on the output response of the spectrometer. Unlike the PCA method, the LDA method can extract the resulting information to optimize the resolution between classes. Therefore, this method was used to detect 5 nectarine cultivars based on the output response of the spectrometer. The results of the identification of figures equal to 100% were obtained.