Characterisation and prediction of carbohydrate content in zucchini fruit using near infrared spectroscopy

Analysis of the chemical composition of natural carbohydrates - An overview of methods

Natural carbohydrates are gaining importance over a wide spectrum of human activity due to their versatile functionalities. The properties of carbohydrates are currently used in many branches of industry and new possibilities of their utilization, like in medicine or materials science, are demonstrated systematically. The attractive properties of carbohydrates result from their chemical structure and ability to form macromolecules and derivatives. Each application of carbohydrate requires a knowledge of their chemical composition, which due to the number and differentiation of monosaccharides and their spatial forms is often challenging. This review presents an overview on sample preparation and the methods used for the determination of the fine chemical structure of natural carbohydrates. Most popular and reliable colorimetric, chromatographic and spectroscopic methods are presented with an emphasis on their pros and cons.

Determination of Quality Parameters in Mangetout (Pisum sativum L. ssp. arvense) by Using Vis/Near-Infrared Reflectance Spectroscopy

Pisum sativum L. ssp. arvense, is colloquially called tirabeque or mangetout because it is eaten whole; its pods are recognized as a delicatessen in cooking due to its crunch on the palate and high sweetness. Furthermore, this legume is an important source of protein and antioxidant compounds. Quality control in this species requires the analysis of a large number of samples using costly and laborious conventional methods. For this reason, a non-chemical and rapid technique as near-infrared reflectance spectroscopy (NIRS) was explored to determine its physicochemical quality (color, firmness, total soluble solids, pH, total polyphenols, ascorbic acid and protein content). Pod samples from different cultivars and grown under different fertigation treatments were added to the NIRS analysis to increase spectral and chemical variability in the calibration set. Modified partial least squares regression was used for obtaining the calibration models of these parameters. The coefficients of determination in the external validation ranged from 0.50 to 0.88. The RPD (standard deviation to standard error of prediction ratio) and RER (standard deviation to range) were variable for quality parameters and showed values that were characteristic of equations suitable for quantitative prediction and screening purposes, except for the total soluble solid calibration model.

Purple eggplant and zucchini color, mechanical properties, mastication, and sensory perception influenced by boiling and grilling

The present study investigated the influence of boiling and grilling deployed at 15, 30, and 45 min on selected eggplant and zucchini qualities. The colorants present in these vegetables contribute to their attractive appearance while presenting health-benefit components. This study shows that boiling and grilling differently affected the color properties of the eggplant and zucchini flesh and skin, leading to discoloration. However, the multifactorial mechanisms behind this phenomenon are not yet fully uncovered. Both boiling and grilling caused softening in examined vegetables within cooking prolongation, but different effects were observed. In the case of grilling, large- and small-deformation mechanical parameters established higher values. Thus, grilling produced firmer samples, which was further projected on mastication and dynamic sensory perception. Although there was limited differentiation in mastication parameters, it was observable that grilled products were more mastication-demanding. The number of chews and consumption time provided most of the information and established higher values for short-time cooking treatments and grilling. As a consequence, samples that had longer in-oral exposure times were perceived as bitter (eggplant) or sweet (zucchini). Oppositely, boiled vegetables were juicier. Regardless of the type of vegetable or cooking method, the shortest cooking treatments resulted in products dominantly perceived as firm.

Modified specific gravity method for estimation of starch content and dry matter in cassava

An empirical model for the estimation of starch content (SC) and dry matter (DM) in cassava tubers was developed as an alternative method to polarimetry and dry oven. These improved estimation equations were developed based on the specific gravity (SG) method. To improve accuracy, the one hundred-seventy-four sample were obtained from four commercial varieties of cassava in Thailand including KU50, CMR38-125-77, RY9 and RY11, respectively. The age of sample collected from four to twelve months after planting was used in this experiment. The empirical model was created from their relationships between SG obtained from small sample size (~100 g) and its SC and DM. The SG for cassava was strongly correlated with the SC and DM, with values for the coefficient of determination (R²) of 0.81 and 0.83, respectively. The SC showed a high correlation with the DM, with R² of 0.96. To confirm that the empirical model was effective when applied to other samples, unknown samples collected from another area were tested, and the results showed a standard error of prediction (SEP) of 1.02%FW and 3.49%, mean different (MD) of -0.66%FW, -0.89% for the SC and DM, respectively. Hence, our empirical equation based on a modified SG method could be used to estimate the SC and DM in cassava tubers. It can help breeders to reduce costs and time requirements. Moreover, breeders could be used the methods to evaluate the SC and DM from the tuber formation to harvesting stage and monitoring the changes in SC and DM during breeding.

Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

Development of multi-product calibration models of various root and tuber powders by fourier transform near infra-red (FT-NIR) spectroscopy for the quantification of polysaccharide contents

The objective of this study was to quantify the chemical content of multiple products using one single calibration model. This study involved seven tuber and root powders from arrowroot, Canna edulis, cassava, taro, as well as purple, yellow, and white sweet potato, for partial least square (PLS) regression to predict polysaccharide contents (i.e., amylose, starch, and cellulose). The developed PLS models showed acceptable results, with Rc 2 of 0.9, 0.95, and 0.85 and SEC of 2.7%, 3.33%, and 3.22%, for amylose, starch, and cellulose, respectively. The models also successfully predicted polysaccharide contents with Rp 2 of 0.89, 0.95, and 0.79; SEP of 2.83%, 3.33%, and 3.55%; and RPD of 3.02, 4.47, and 2.18 for amylose, starch, and cellulose, respectively. These results showed the potential of Fourier transform near-infrared spectroscopy to quantify the chemical composition of multiple products instead of using one individual model.

Prebiotic potential of pulp and kernel cake from Jerivá (Syagrus romanzoffiana) and Macaúba palm fruits (Acrocomia aculeata)

The jerivá (Syagrus romanzoffiana) and the macaúba (Acrocomia aculeata) are palm trees of the Arecaceae family, widely distributed in tropical and subtropical areas of Latin America, which have a low production cost and high productivity throughout the year. Due to the high content of lipids, their fruits have been used for oil extraction, which generates byproducts such as the pulps and the kernel cakes, a nutritionally rich byproduct that can be added into human food and, may have prebiotic potential. Therefore, the objective of this work was to characterize and evaluate the prebiotic potential of jerivá pulp (JP), macaúba pulp (MP), jerivá kernel cake (JC) and macaúba kernel cake (MC). For this, the fruits characterization was carried out through proximate composition, phenolic compounds content, and antioxidant activity, besides evaluating the antimicrobial and fermentative capacity of Bifidobacterium lactis, Lactobacillus casei, and Lactobacillus acidophilus against Escherichia coli. Jerivá and macaúba pulps and kernel cakes presented high levels of dietary fiber (20.45% JP, 37.87% JC, 19.95% MP and 35.81% MC) and high antioxidant activity, especially JP, which also showed the high values found for ABTS and DPPH (2498.49 µMTrolox·g^-1 fruit and 96.97 g fruit·g^-1 DPPH, respectively), has a high total phenolic content (850.62 mg GAE·100 g^-1). Also, JP promoted a better growth of probiotic strains and a more relevant pH reduction when compared to the commercial prebiotic FOS. However, MP, JC, and MC were also able to favor the growth of the strains. Probiotic microorganisms were able to use JP, MP, JC, and MC and produced short-chain fatty acids such as lactic, propionic, butyric, and acetic acid, capable of promoting health benefits. Therefore, the byproducts from jerivá and macaúba oil extraction have characteristics that indicate their prebiotic potential, and maybe interesting components to increase the nutritional value of foods.

The Profile of Selected Antioxidants in Two Courgette Varieties from Organic and Conventional Production

Courgette is considered as a low-calorie vegetable with health-promoting properties. However, scientific publications focused on the profile and content of bioactive compounds in courgette, as well as the potential fruit quality modulating factors, are rare. Due to the high adaptability of courgette to weather and agronomic conditions, it is produced on a global scale. The aim of this study was to analyse the impact of organic versus conventional agronomic practices on the concentration of selected antioxidants in courgette fruits. Fruits of two courgette varieties (Astra Polka and Nimba) produced in an organic and conventional system were tested by high performance liquid chromatography (HPLC) to determine the content of polyphenols (flavonoids and phenolic acids), carotenoids, chlorophylls, and vitamin C. Organic courgette fruits were characterised by their significantly higher content of phenolic acids and flavonoids when compared to the conventionally grown fruit. The organic cultivation might be a good method to increase concentration of bioactive compounds with antioxidant properties in courgette fruits. Nevertheless, the identified trends should be further confirmed, with attention paid to other courgette varieties, as well as to the potential interactions between the plant genotype, agronomic system and the location-specific growing conditions.

Prediction of starch reserves in intact and ground grapevine cane wood tissues using near-infrared reflectance spectroscopy

BACKGROUND

Near-infrared reflectance spectroscopy (NIRS) technology can be a powerful analytical technique for the assessment of plant starch, but generally samples need to be freeze-dried and ground. This study investigated the feasibility of using NIRS technology to quantify starch concentration in ground and intact grapevine cane wood samples (with or without the bark layer). A partial least squares regression was used on the sample spectral data and was compared against starch analysis using a conventional wet chemistry method.

RESULTS

Accurate calibration models were obtained for the ground cane wood samples (n = 220), one based on 17 factors (R² = 0.88, root mean square error of validation (RMSEV) of 0.73 mg g^-1 ) and the other based on 10 factors (R² = 0.85, RMSEV of 0.80 mg g^-1 ). In contrast, the prediction of starch within intact cane wood samples was very low (R² = 0.19). Removal of the cane bark tissues did not substantially improve the accuracy of the model (R² = 0.34). Despite these poor correlations and low ratio of prediction to deviation values of 1.08-1.24, the root mean square error of cross-validation (RMSECV) values were 0.75-0.86 mg g^-1 , indicating good predictability of the model.

CONCLUSIONS

As indicated by low RMSECV values, NIRS technology has the potential to monitor grapevine starch reserves in intact cane wood samples. © 2020 Society of Chemical Industry.