Fruit quality assessment of organic plums grown in Norway

Fruits and vegetables play an important role in the human diet worldwide. Safety and quality are important considerations for the purchase and consumption of fresh raw products. Since the consumers increasingly insist on a very high fruit and vegetable quality nowadays, it is also necessary to provide information about the origin and traceability of these products. One of the alternatives to traditional wet chemistry and human evaluation strategies, which allow quality assessment and control of fruits and vegetables, is vibrational spectroscopy. During the last decade, several vibrational spectroscopic methods have been developed, and have become more widely adopted by the agricultural and food sectors. These methods offer the possibility to rapidly measure several properties of the sample with minimal sample preparation and often to perform nondestructive quality evaluation. In this chapter, an overview of near‐infrared, mid‐infrared, and Raman spectrometries and infrared thermography is given for use in measuring quality attributes of horticultural produce.

The present study aimed to assess seven tomato genotypes (commercial cultivars and local landraces) with respect to yield potential, physicochemical and sensory properties when cultivated under organic conditions, over two growing seasons. Traits like yield ingredients and fruit quality were measured whereas, the morphological traits were assessed according to the UPOV classification, starting from the seedling stage until the stage of fruit maturity. Total yield per plant and cultivar determined, while assessment of fruit quality was performed on the basis of physicochemical characteristics (content in vitamin C, acidity, pH of the juice, soluble materials). Genotype fingerprinting was performed by means of RAPD analysis. Following a preliminary evaluation of 50 RAPD primers, 10 polymorphic primers were selected and used in RAPD-PCRs. Statistical data analysis revealed significant differences in a range of characteristics under study whereas at the same time, yield was significantly different both among the varieties and the harvest stages. Interestingly, the landraces tested, especially Homiricon and Lotos, presented important organoleptic attributes under organic growing conditions and were met with high consumer acceptance, thus suggesting their possible direct exploitation in the market or after the application of an intravarietal selection breeding program.

Daily consumption of fruits has rendered sophisticated techniques for accurate evaluation of its quality and how it can be initiated in a more rapid way has become a state-of-art for the fruit processors and food safety agencies. With the advent of non-destructive techniques in the past two decades, the quality and safety assessment procedure has become more reliable and non-invasive with reasonable accuracy. They have significantly contributed to the quality and safety inspections of fruits allowing for minimal expense of processing systems. The current review provides an overview of the technical aspects used for evaluating the essential physicochemical components in fruits. The non-destructive techniques described in this study primarily constitute near-infrared (NIR) spectroscopy and hyperspectral imaging in addition to their respective data analysis, classification, and calibration methods. First, the individual mechanism is outlined considering spectral/image acquisition mode followed by the extraction of information from the acquired data. Then, potentials of different chemometrics, image processing, and hyperspectral data reduction and feature extraction methods used in both the techniques have been epitomized for predicting the fruit maturity and other internal components such as firmness, acidity, phenolic content, soluble solids, vitamins C, moisture content, defects, fecal contamination, starch index, sugar content, and dry matter. The literature in this overview portrays the potentials of different chemometric and multivariate image analysis methods used in near-infrared spectroscopy and hyperspectral imaging, respectively, as excellent quality assessment aspects for fruits. However, further improvements are required in handling the voluminous data in industrial applications. The application of both the techniques is limited to a few fruits and further research is required for exploiting their implications.

Development of ultrasound technique has not been progressing for evaluating the internal quality of fruits as fast as that of processed foods. In this research for quality assessment of pear fruit (Shah Miveh variety) an ultrasonic measurement system was constructed to transmit and receive the ultrasonic waves. The apparatus included a pulser-receiver, a pair of 75 kHz ultrasonic transducers with exponential horn, and a computer system for data acquisition and analysis. Several mechanical and chemical properties, including firmness, TSS, acidity, elastic modulus, pH and total dry matter for destructive quality assessment were measured. Velocity and attenuation of ultrasonic waves for nondestructive tests were also measured. The fruit quality levels for the experiment were: unripe, ripe and overripe. The results of tests showed that firmness was the best parameter for measuring fruit quality, as it decreased significantly with ripeness. The effect of ripeness on the velocity and attenuation of ultrasonic waves was also significant. Investigation showed a positive linear relationship between fruit firmness and wave velocity (R2=0.81). Furthermore, the relationship between fruit firmness and attenuation was exponential and wave attenuation decreased with increasing fruit firmness (R2=0.895). The Relationship between ultrasonic properties and fruit modulus of elasticity showed that the wave velocity increased and attenuation decreased with ‌increasing elasticity. It can be concluded that the ultrasonic instrument equipped with exponential horns can effectively be utilized for pear quality assessment based on measurement of wave velocity and attenuation.

ABSTRACTNear‐Infrared (NIR) Spectroscopy has become a pivotal non‐destructive technology in agricultural production and fruit quality assessment, particularly in the pear industry. This review comprehensively examines recent advancements in NIR Spectroscopy for evaluating critical quality parameters such as Soluble Solids Content (SSC), firmness, and moisture content, which are essential for determining optimal harvest timing, improving storage practices, and extending shelf life. The evolution of portable NIR spectrometers is explored, emphasizing their increasing suitability for real‐time, in‐field applications while addressing challenges such as calibration variability and environmental factors. Additionally, the integration of NIR spectroscopy with imaging technologies like Hyperspectral Imaging (HSI) and Multispectral Imaging (MSI) is highlighted for their potential to enhance the precision and scope of fruit quality assessment. The review further considers incorporating low‐cost technologies, such as electronic noses and machine vision, with NIR spectroscopy to create cost‐effective and robust multi‐sensor platforms. By synthesizing these developments, the review identifies gaps in current methodologies, such as limited model generalization across diverse pear varieties, and proposes solutions like data fusion and advanced machine learning approaches. These strategies are poised to improve model adaptability and accuracy, ensuring NIR spectroscopy remains at the forefront of pear quality assessment. Future research directions emphasize the need for integrating cutting‐edge technologies with NIR spectroscopy to achieve more sustainable and efficient agricultural practices.

Forchlorfenuron synthetic cytokinin CPPU, a synthetic cytokinin, is commonly used to enhance plant growth, but concerns about its potential environmental impact and residue accumulation have prompted the search for more sustainable alternatives. So, a field trial was conducted to assess the potential of seaweed extract (SWE) as a natural substitute for CPPU in compensating for its role. Foliar spraying with both SWE (0, 2, and 4 mg L-1) and CPPU (0, 50, and 100 mg L-1) was carried out either individually or in all combination. The evaluation included the assessment of vegetative growth performance, flowering traits, yield, and fruit quality. Results indicated that spraying SWE at the highest concentration (4 mg L-1) demonstrated significant positive effects in all the studied characteristics. Similarly, CPPU at the highest concentration (100 mg L-1) when applied as a foliar spray individually exhibited significant positive impacts on all studied traits. Furthermore, combining SWE at 4 mg L-1 with CPPU at 100 mg L-1 resulted in significant increases in most of the studied parameters. In conclusion, the findings suggest the potential use of foliar spraying with SWE as an effective alternative to CPPU for enhancing squash growth performance and yield in arid regions.

Fluorescence and reflectance are two different forms of light interaction with the matter, and they can be complementary in measuring fruit quality and condition. The objective of this research was to develop an integrated hyperspectral reflectance and fluorescence imaging system for measuring apple maturity. Experiments were performed to acquire hyperspectral laser induced fluorescence and reflectance scattering images and measure multiple maturity parameters (flesh and skin color, firmness, soluble solids, starch, and acid) from 'Golden Delicious' apples harvested over a 4-week period. Fluorescence and reflectance scattering profiles were described by a two-parameter Lorentzian function. Multi-linear regression prediction models were developed relating Lorentzian parameters to individual maturity parameters for each sensing mode and their combined data. The fluorescence prediction models had consistently lower correlations with individual maturity parameters than did reflectance. The integration of reflectance and fluorescence improved maturity measurements over either reflectance or fluorescence; the improvements in correlation were noticeable for most parameters (up to 12% for titratable acid). Since fluorescence and reflectance measurements can be performed with the same imaging system in real time, the integrated technique can provide better assessment of apple fruit maturity and quality.

The assessment of blueberry fruit quality is traditionally conducted through laboratory equipment. Despite its high accuracy, this method remains destructive, labor-intensive, time-consuming, and costly. Consequently, there is a pressing need for innovative solutions such as image-based and artificial intelligence (AI)-driven analysis. To address these limitations, this study aimed to analyze whether an approach based on mobile image-based analysis combined with machine learning (ML) algorithms could develop a non-destructive framework for evaluating blueberry fruit quality, specifically focusing on total soluble solids (TSS) and firmness. Firstly, we collected numerous blueberry samples during the maturity stage to construct a comprehensive dataset. These samples were meticulously analyzed in a laboratory for diameter, TSS, firmness, and color. Simultaneously, RGB images were captured using a mobile device. These images were processed to extract spectral bands (red, green, and blue). Eight ML algorithms were employed to develop predictive models capable of predicting the qualitative parameters of the blueberries. Initially, correlation analysis demonstrated that RGB images suggestively contribute to fruit quality assessment (r<0.41). However, the integration of ML algorithms significantly enhanced the predictive accuracy of these models (R2=0.71-0.99, MAE=0.003-0.28, and RMSE=0.004-0.31). This innovative approach differs from existing methods by providing a non-destructive, cost-effective, and efficient alternative to assess blueberry fruit quality. Our findings, therefore, confirm the applicability of mobile images for supporting high-quality blueberry harvesting. These advancements can potentially reduce labor costs, increase yield quality, and support advancements in precision agriculture.

Detection of Chemically Matured Mango Fruits Using Laplacian Descriptors and Scale Determinants

Persimmon (Diospyros kaki Thunb.) is becoming a fruit that is used worldwide because it contains high nutritional and medicinal value. However, the trait evaluation of persimmon is still needed and is critical for marketing and breeding, especially in China. Here, we evaluated thirteen quality indicators (fruit weight, horizontal length, vertical length, firmness, titratable acid content, vitamin C content, flavonoid content, anthocyanin content, soluble sugar content, pectinase activity, soluble protein content, tannin content, and tannin cell size) in six Chinese pollination-constant, astringent (PCA) persimmon cultivars (‘Jinxi 3′, ‘Ganfang 1′, ‘Poyang 5′, ‘Poyang 6′, ‘Yifeng 1′, and ‘Yifeng 3′) and a Japanese pollination-constant non-astringent (PCNA) persimmon cultivar (‘Youhou’). The quality indicator data were normalized and subjected to variant analysis and principal component analysis (PCA). The results showed that 10 of 13 indicators among the seven persimmon cultivars were significantly different, and the contribution rates of the first principal component reached 40.582%. The principal component comprehensive scores for ‘Poyang 5′ and ‘Ganfang 1′ ranked second and third, respectively, and were clustered with that of ‘Youhou.’ In addition, we also measured the expression levels of three stress resistance genes and three proanthocyanidin (PA, also called condensed tannins) pathway genes in these persimmon cultivars by quantitative reverse transcription PCR (qRT—PCR). The qRT—PCR analysis of DkCBF and DkWRKY3/8 showed low resistance to cold in ‘Ganfang 1′ but stronger resistance to anthracnose. Moreover, the expression of the PA pathway genes demonstrated that the PA content in ‘Ganfang 1′ was at a moderate level in the seven varieties. Together, our study revealed relatively comprehensive profiles of persimmon quality evaluation and demonstrated that ‘Ganfang 1′ may have the potential to be used as a breeding parent for future persimmon breeding programs.

The assessment of fruit quality is important for the cultivator and the consumer. The paper describes the image processing technique to estimate the volume and maturity of sweet lime. Spherical shape fruit is only considered for this analysis. The image is captured using a high resolution digital camera and cell phone. An algorithm is developed in MATLAB for finding the volume and maturity of the sweet lime. Volume of the fruit is computed by estimating the radius of the image. Maturity is determined with the RGB color coding based on RG ratio. The results are validated experimentally by measurement using Vernier Calipers.

Selection of a proper cultivar is one of the most important management decisions when investing into apple orchard. This paper presents a methodology for evaluation of suitability for cultivation of some apple cultivars by using a multi-criteria model based on analytical hierarchy process. The software tool Expert Choice was used to support growers in making decisions about which new apple cultivar to grow. The model was applied to 13 cultivars with data derived from questionnaires completed by group of experts of different fields (orchardists, experts for fruit production economics and fruit marketing, cultivar breeders, experts of state service and commission for introduction of new cultivars, experts for fruit storage and fruit quality assessment, plant protection and fruit growing advisers). The results are shown as priority for individual cultivar. The potential of the model for assessing apple cultivar is demonstrated with the aim of providing a comprehensive explanation and justification of the assessment technique. It also indicates strong and weak points (in market potential, fruit estimation, production demands and tree estimation) of each assessed cultivar.

ABSTRACTOver the past decades, imaging and spectroscopy techniques have been rapidly developing and widely applied in nondestructive fruit and vegetable quality assessment. The physical properties (including size, shape, color, position, and temperature) and biological properties (including cultivar, season, maturity level and geographical origin) of fruits and vegetables vary from one to another. A great variety of physical and biological properties of agricultural products influence the optical propagation properties and interaction behaviors with incident light, thus decreasing the quality inspection accuracy. Many attempts have been made in image correction and spectral compensation methods to improve the inspection accuracy. This paper gives a detailed summary about influence of physical and biological variability, as well as the correction and compensation methods for eliminating or reducing the effects in fruit and vegetable quality nondestructive inspection by using imaging and spectroscopy techniques. The advantages and disadvantages of the solution methods are discussed and summarized. Additionally, the future challenges and potential trends are also reported.

A nondestructive approach for fruit quality assessment is outlined. Fruit Quality is determined by external and internal features. Only external features won’t help to determine the overall quality of a fruit. Color images and x-ray image of Orange and Banana fruit are captured. With Image processing tool features like Color histogram, color moments and Gabor filter are extracted for external as well as internal quality assessment. Total 18 color features and 27 textural features are extracted for analysis. Sample color images and x-ray image of Orange and Banana fruit are trained and tested. Fusion algorithm is developed to detect the fruit class. Fruits are divided into three classes’ viz. 50% defected, 90% defected and Normal class. SVM and ANN classifier are elected to determine the fruit quality. This information is used to determine degradation level of a fruit. The experimental result showed that % accuracy with SVM classifier is more than ANN classifier.

Status and trends of artificial intelligence in the R & D of future fruit & vegetable juice