Effect of cultivar and season on the robustness of PLS models for soluble solid content prediction in apricots using FT-NIRS

Abstract

FT-NIR models were developed for the nondestructive prediction of soluble solid content (SSC), titratable acidity (TA), firmness and weight of two commercially important apricot cultivars, ''Hacıhaliloglu'' and ''Kabaaşı'' from Turkey. The models constructed for SSC prediction gave good results. We could also establish a model which can be used for rough estimation of the apricot weight. However, it could not be possible to predict accurately TA and firmness of the apricots with FT-NIR spectroscopy. The study was further extended over 3 years for the SSC prediction. Validation of the both mono and multi-cultivar models showed that model performances may exhibit important variations across different harvest seasons. The robustness of the models was improved when the data of two or three seasons were used. It was concluded that in order to developed reliable SSC prediction models for apricots the spectral data should be collected over several harvest seasons.

Non-destructive near infrared spectroscopy externally validated using large number sets for creation of robust calibration models enabling prediction of apple firmness

“…Previous reports confirmed that the extent of the seasonal variations depended on the cultivar. 14,17 The datasets used should then represent the existing diversity in the fruit cultivar itself. 8 The best individual models have to be available for each cultivar, season and origin.…”

“…The NIR region of the electromagnetic spectrum covers wavelengths between 800 and 2500 nm (equivalent to wavenumbers 12,500-4000 cm À1 ) and is widely used in academia and industry. 9 Instruments previously developed for engineering materials have already been applied for the agricultural products to measure the vibration of the chemical bonds, that is, characteristic, that can be correlated with firmness and ripeness, 4,9,11 soluble solid content, [12][13][14][15] titratable acidity, 1,16 dry matter, 17 as well as sugar and water content 8 or moisture. 18 Although flesh firmness being a physical parameter cannot be directly measured using NIR spectroscopy, it is possible to create calibration models correlating spectral responses due to the chemical components of the fruit such as pectins and carbohydrates with the texture properties including firmness.…”

“…18 Although flesh firmness being a physical parameter cannot be directly measured using NIR spectroscopy, it is possible to create calibration models correlating spectral responses due to the chemical components of the fruit such as pectins and carbohydrates with the texture properties including firmness. 14 These components and their contents change with time and so do the spectra. Thus, it is possible to follow the changes in the quality of the fruit during storage.…”

Effect of spectrum measurement position on detection of Klason lignin content of snow pears by a portable NIR spectrometer

“…In the CARS algorithm, variables with smaller absolute values of the regression coefficient were forced to be removed, but the elimination of variables may have contained useful information because of the absolute value changed with the variations of the sample space. A genetic algorithm (GA) was used to select effective variables for determination of the soluble solids concentration (SSC) in apples, pears, oranges, apricots, and garlic by Li (Li, Huang, et al, 2018), Puneet Mishra (Puneet Mishra, Woltering, Brouwer, & Hogeveen‐van Echtelt, 2021b), Song (Song et al, 2019), Ozdemir (Ozdemir et al, 2019), and Rahman (Rahman et al, 2018), respectively. Camps (Camps & Camps, 2019) used FT‐NIR spectroscopy on peeled tubers combined with a GA method to predict the reducing sugars and dry matter in potatoes.…”

Rapid non‐destructive analysis of lignin using NIR spectroscopy and chemo‐metrics

“…Snow pear, a common and popular fruit in China, has been demonstrated as traditional remedies for relieving respiratory symptoms, constipation and alcoholism in traditional Chinese medicine (TCM) (Li et al, 2012) for over 2000 years. Near‐Infrared (NIR) spectroscopy (Ibáñez et al, 2019; Song et al, 2019; Zhang et al, 2018), combined with processing analytical technology have proven to be a useful and non‐destructive spectral techniques (Ibáñez et al, 2019; Ozdemir et al, 2019) for rapid quantitative analysis of internal chemical components in fruits and food (Xia et al, 2018).…”