Discrimination of E uterpe oleracea  Mart. (Açaí) and E uterpe edulis  Mart. (Juçara) Intact Fruit Using Near-Infrared (NIR) Spectroscopy and Linear Discriminant Analysis

Abstract

As the verification of authenticity of agricultural foods has become a potential application of spectroscopic methods, this study aimed to use near-infrared (NIR) spectroscopy associated with linear discriminant analysis (LDA) to discriminate intact fruit of the species Euterpe oleracea Mart. (açaí) and Euterpe edulis Mart. (juçara). One hundred sixty-eight açaí fruits from 17 genotypes and 200 fruits from 20 juçara genotypes were investigated and two groups were used for discriminating the species: calibration (294 fruits) and prediction (74 fruits). L*, a*, b*-PCA, and NIR-PCA of spectra pretreated with multiplicative scatter correction (MSC) showed the best results. The L*, a*, b*-LDA model resulted in 96.3% correct classification and 93.2% prediction accuracy of the external validation group. NIR spectra pretreated with MSC had 98% correct classification and 97.3% prediction accuracy. NIR spectroscopy associated with LDA is a reliable method for the discrimination of intact açaí and juçara fruit. PRACTICAL APPLICATIONSRecently, there has been an increasing interest by food industry and producers to assure consumers the authenticity of their products. Many food properties are related to individual compounds and their active chemical ingredients, such as essential oils, terpenoids, flavonoids, phenolic compounds, amino acids and organic acids. The Euterpe genus has many species of economic interest and E. oleracea (açaí or assai), E. edulis (juçara) and E. precatoria are among the most important species in the agribusiness sector. As the fruit of these species are morphologically similar, it is important to develop instrumental methods to sort these fruit at the convey belt in order to ensure authenticity and near-infrared (NIR) spectroscopy can be used for that. Based on our results, the NIR spectroscopy and chemometrics provide a useful approach for authenticating fruit from Euterpe genus and can be used by the food industry to identify adulteration.

Effect of in vitro gastrointestinal digestion on the mineral content, phenolic compounds, and antioxidant capacity of commercial pulps of purple and white açaí (Euterpe oleracea Mart.)

“…´) or purple (purple-ac ¸aı ´) colour, according to the botanical variety of the species. In contrast, the mesocarp has a thickness of 1-2 mm, is pulpy and is of variable colour, which may originate purplish pulps when originating from the purple-ac ¸aı ´or cream/greenish when coming from the white-ac ¸aı ´; the endocarp corresponds to 85-95% of the fruit, exhibiting a hard, brownish yellow seed (Dall'Acqua et al 2015;Neri-Numa et al 2018).…”

Sensory Analysis and Consumers Studies of Açai Beverage After Thermal, Chlorine and Ozone Treatments of the Fruits

“…This discoloration may be related to anthocyanins degradation increased by this temperature, as the parameter b * since it was related to the concentration of anthocyanins on the surface of intact Açai fruit (Dall' Acqua et al . ). These values were used to determine the chroma C * and h ° attributes, indexes considered most appropriate color measurement, and are analogous to saturation or intensity (McGuire ).…”

“…The L * values were achieved on beverages from fruits blanched at 90C were lower than L * values obtained in intact Açai fruits (30.35–31.90) (Dall' Acqua et al . ), indicating that this temperature may have led to a greater darkening of the beverage. Regarding the coordinate a *, the treatment of the fruit with 150 mg/L −1 chlorination showed a lower value than the other treatments, being characterized as less red.…”

Quality evaluation of intact açaí and juçara fruit by means of near infrared spectroscopy

“…(Pessoa and Teixeira, 2012), with resulting impact. Due to the spectral differences of açaí and juçara populations Dall' Acqua et al (2015) reported that FT-NIR spectra pretreated with multiplicative scatter correction (MSC) had 98% correct classification and 97.3% prediction accuracy in discriminating intact açaí and juçara fruits. As a robust model should contain the maximum variation to be expressed in the prediction sets the development of a separate model may be justified (Golic and Walsh, 2006).…”