Characteristic Volatiles and Cultivar Classification in 35 Apple Varieties: A Case Study of Two Harvest Years

Investigating the potential of yacon (Smallanthus sonchifolius) juice in the development of organic apple-based snacks

This study investigates the potential of yacon (Smallanthus sonchifolius) juice for the development of prebiotic-rich organic apple-based snacks. Yacon syrup, primarily composed of fructan, inulin, fructooligosaccharides (FOS), and free sugars, represents a promising nutraceutical product. Its great potential in food processing, particularly as an innovative source of prebiotics, has been demonstrated both in vitro and in vivo since it is fermented specifically by lactobacilli and bifidobacteria. Our objective was to explore the feasibility of employing vacuum impregnation process to incorporate yacon juice into organic apples, followed by hot air drying for the formulation of dried organic apple-based snacks with health-enhancing attributes. We assessed the prebiotic and physicochemical characteristics of the impregnated snacks, also considering 50 days of storage at room temperature. Vacuum impregnation and air drying produced dried apple slices impregnated with yacon juice with good quality and stability. Higher levels of fructan (16-fold difference compared to non-impregnated apples) in the apple slices increased their prebiotic potential, promoting the growth and viability of cells within simulated intestinal fluid, including strains of Bifidobacterium animalis subsp. lactis BB -12, Bifidobacterium breve DSM 20091, Bifidobacterium longum subsp. infantis DSM 20088, Lacticaseibacillus rhamnosus GG and Lacticaseibacillus rhamnosus C112, even after prolonged storage. Remarkably, the physicochemical parameters of the impregnated and dried apple slices remained nearly constant and akin to the control samples. Therefore, the combination of vacuum impregnation and air drying has the potential to be used to produce enriched prebiotic organic apple snacks, providing consumers with additional health benefits, including enhanced gut health, with its associated implications, and increased satiety. This innovation could contribute to the development of health-promoting food products with improved nutritional profiles.

A non-invasive method for phenotyping scab-tolerant apple plants using volatile organic compounds

One of the most devastating diseases of apples is scab, caused by the fungus Venturia inaequalis. Most commercial apple varieties are susceptible to this disease; only a few are resistant. Breeding approaches are being used to develop better apple varieties that are resistant to scab. Volatile organic compounds (VOCs) contribute greatly to a plant's phenotype, and their emission profile largely depends on the genotype. In the non-destructive phenotyping of plants, VOCs can be used as biomarkers. In this study, we assessed non-destructively the scab tolerance potential of resistant (cv. 'Prima') and susceptible (cv. 'Oregon Spur') apple cultivars by comparing their major leaf VOC compositions and relative proportions. A comparison of the leaf VOC profiles of the two cultivars revealed 16 different VOCs, with cis-3-hexenyl acetate (3HA) emerging as a biomarker of cultivar differences. V. inaequalis growth was significantly inhibited in vitro by 3HA treatment. 3HA was significantly effective in reducing scab symptoms on V. inaequalis-inoculated leaves of 'Oregon Spur.' The resistant cultivar 'Prima' also exhibited higher lipoxygenase (LOX) activity and α-linolenic acid (ALA) levels, suggesting that V. inaequalis resistance is linked to LOX activity and 3HA biosynthesis. This study proposes 3HA as a potential biomarker for rapid non-destructive screening of scab-resistant apple germplasm of 'Prima' based on leaf VOCs.

Fruit Quality Attributes of Organically Grown Norwegian Apples Are Affected by Cultivar and Location

In this work, 12 apple cultivars grown organically in three regions of Norway (Telemark, Ullensvang, Viken) were analyzed in terms of fruit quality, with the aim of equating different growing regions under specific climatic conditions. Apples were analyzed for concentration levels of minerals, sugars, sugar alcohols, organic acids, total phenolic content (TPC), radical scavenging activity (RSA), and phenolic profiles. Discovery "Rose" from Telemark stored the highest level of minerals (24,094.5 mg/kg dry weight). Glucose, fructose, sucrose, and sorbitol were the major carbohydrates, whereas the predominant organic acids were quinic acid and malic acid. Cultivar Discovery from Ullensvang had the highest TPC (9.22 g/kg) and RSA (229.32 mmol TE/kg). Of the polyphenols quantified, chlorogenic acid and kaempferol-3-O-glucoside were the most abounded, accounting for 85.50%. Principal component analysis (PCA) shows that the Ullensvang region is the richest source of most carbohydrates, organic acids (quinic, shikimic, and galacturonic), and most polyphenols, whereas the highest content of minerals and maleic acid characterized Viken. Regardless of location, the Discovery cultivar had, on average, the highest sugar and polyphenol contents. The results obtained suggest that organic apples from Norway are a rich source of beneficial compounds that can have a positive impact on human health. In addition, these results may be useful for consumers in identifying apple cultivars with desirable characteristics and for the fruit industry in tracing back the origin of apples. The findings could also be of great interest for locations with similar climate and soil conditions worldwide.

Aroma-based discrimination of Egyptian versus Indian guava fruits and in response to probiotics as analyzed via SPME/GC-MS and chemometric tools

Guava tree (Psidium guajava L., Myrtaceae) is an economic grown worldwide, particularly in tropical and subtropical regions. Guavas encompass numerous cultivars (cvs.) that were discriminated in previous studies based on leaf morphological features and profile of volatile organic compounds (VOCs). Nevertheless, fruit VOCs have also shown outstanding potential for discrimination of other plant taxa, which has not been utilized in guava. Hence, the current study investigates the various guava cvs. harvested from India and Egypt. A total of 5 samples were analyzed by solid phase microextraction coupled to gas chromatography/mass spectrometry. Results led to the detection of 42 VOCs belonging to aldehydes, alcohols, esters, ketones, aliphatic and aromatic hydrocarbons, in addition to monoterpene and sesquiterpene hydrocarbons. Butylated hydroxytoluene and β-caryophyllene were predominant reaching 77% and 41% in Egyptian and Indian guava, respectively. The impact of probiotic fermentation, i.e., Lactobacillus acidophilus and L. plantarum on aroma profile was not significantly different (p > 0.05). Multivariate data analyses were further applied for samples classification and markers determination, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). PCA score plot showed clear segregation of Egyptian from Indian specimens, whereas OPLS-DA revealed that β-caryophyllene was associated with white fruit versus 3-butenyl isothiocyanate and muurolol in red fruit type in the case of Indian guava. The richness of Egyptian guava in butylated hydroxytoluene in addition to the presence of vitamin C may potentiate its antioxidant activity, to be followed in subsequent studies regarding its health effects.

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review

Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.

Prediction of Apple Hybrid Offspring Aroma Based on Hyperspectral

Used Random forest algorithm to construct a prediction model of aroma components based on the hybrid offspring of 'Honeycrisp' × 'Maodi', and different preprocessing methods were tried (Standardization (SS), First-order Derivative (D1) and Standard normal variate (SNV)). The aroma composition and content were determined by gas chromatography-mass spectrometry (GC-MS), and the main aroma components of apples were classified according to compound categories, including ester, aldehyde, ketone, alcohol. Taking the chemical groups as the research objects, the characteristic wavelengths were selected by grid search algorithm, and the characteristic wavelength-aroma chemical group model was established, and the same method was used to construct the model for single aroma components. The results show: SNV has the best noise removal effect among the five preprocessing methods. Under the SNV treatment, aroma chemical groups of apples showed a good correlation with the spectrum. The number of characteristic spectra of ester are 413, 493, 512, 551, 592, 600, 721, 727, 729, 733 nm, all in the visible light range. The determination coefficient (R2), the root mean square error (RMSE) and the ratio of the standard deviation values (RPD) of validation were 0.90, 4936.16 and 1.13. The characteristic spectrum of alcohols is 519, 562, 570, 571, 660, 676, 737, 738 nm, the range is close to that of ester. The R2 and RMSE of alcohol validation are 0.92 and 83.21, and RPD is 1.30. The number of characteristic spectra of aldehyde is 20, and the most important band is 1000 nm, which is outside the visible light range. The number of characteristic spectra of ketone is 15, and also has some distribution outside the visible light range. The R2 of aldehyde and ketone validation are 0.84 and 0.86. Except for cyclooctanol, the R2 of single aroma compound prediction model performed poorly. Based on the models, we tried to visualize alcohol, which can roughly represent their distribution on apple. Their distributions all show significant differences in the center and edge of apple, but the results are still rough due to the accuracy of models. In conclusion, the study can preliminarily prove that hyperspectral imaging technology (HSI) can perform non-destructive detection of aroma in apple hybrid offspring.

Characterization and Discrimination of Apples by Flash GC E-Nose: Geographical Regions and Botanical Origins Studies in China

Forty-one apple samples from 7 geographical regions and 3 botanical origins in China were investigated. A total of 29 volatile compounds have been identified by flash GC E-nose. They are 17 esters, 5 alcohols, 3 aldehydes, 1 ketone, and 3 others. A principal component analysis was employed to study the relationship between varieties and volatiles. A partial least squares discriminant analysis (PLS-DA), stepwise linear discriminant analysis (SLDA), and decision tree (DT) are used to discriminate apples from 4 geographical regions (34 apple samples) and 3 botanical origins (36 apple samples). The most influential markers identified by PLS-DA are 2-hexadecanone, methyl decanoate, tetradecanal, 1,8-cineole, hexyl 2-butenoate, (Z)-2-octenal, methyl 2-methylbutanoate, ethyl butyrate, dimethyl trisulfide, methyl formate, ethanol, S(-)2-methyl-1-butanol, ethyl acetate, pentyl acetate, butyl butanoate, butyl acetate, and ethyl octanoate. From the present work, SLDA reveals the best discrimination results in geographical regions and botanical origins, which are 88.2% and 88.9%, respectively. Although machine learning DT is attempted to classify apple samples, the results are not satisfactory.