Abstract
This research aimed to create near infrared (NIR) spectroscopy models for the classification of saline water with a pattern recognition technique. A total of 112 water samples were collected from the Tha Chin river basin in Thailand. Water samples with salinity less than 0.2 g/l were identified as suitable for agriculture, while water samples with salinity higher than 0.2 g/l were found to be unsuitable. The NIR spectra of water samples were recorded using a Fourier transform (FT) NIR spectrometer in the wavenumber of 12,500–4,000 cm-1. The salinity of each water sample was analysed by electrical conductivity meter. Identification models were established with 5 supervised pattern recognition techniques including k-nearest neighbour (k-NN), support vector machine (SVM), artificial neural network (ANN), soft independent modelling of class analogies (SIMCA), and partial least squares-discriminant analysis (PLS-DA). The performance of the NIR model was carried out with a split-test method. About 80% of spectra (90 spectra) were randomly selected to develop the classification models. After model development, the NIR spectroscopy models were used to classify the categories of the remaining samples (22 samples). The ANN model showed the highest performance for classifying saline water with precision, recall, F-measure and accuracy of 84.6%, 100.0%, 91.7% and 90.9%, respectively. Other techniques presented satisfactory classification results with accuracy greater than 68.2%. This point indicated that NIR spectroscopy coupled with the pattern recognition technique could be applied to classify saline water for agricultural use according to salinity level in natural resources.
Highlights
IntroductionIt is an important river for agricultural plantations, especially the lower part of the river, where many kinds of fruits such as mangoes and coconuts as well as valuable
Tha Chin river basin is located in the central region of Thailand and is separated from the Chao Phraya River
The water samples were placed in BOD bottles at a volume of 300 ml, after which they were delivered to the laboratory for scanning near infrared (NIR) spectra
Summary
It is an important river for agricultural plantations, especially the lower part of the river, where many kinds of fruits such as mangoes and coconuts as well as valuable. From 2014 to 2018, the income gained by exported orchids was around 1.9–2.3 billion baht (about 54–66 million USD) per year [1] The key to this success was the quality of the soil and water making high-quality, valuable products. The general principle is to determine the properties of water, i.e. the electrical conductivity by conductometer, the specific gravity by hydrometer, the angles of incidence and reflection of light by refractometer and the total dissolved solids (TDS) These methods can be done but their results can be disturbed by other chemicals, especially other ionic salts, leading to erroneous experiment results. After achieving the most feasible model, the NIR spectroscopy models could be applied to more efficiently manage water, leading to reduced risks for agricultural product damage
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