Low-cost and handheld detector on soluble solids content and firmness of kiwifruit

Zhou, Yihang; Ji, Tongkui; Du, Rongyu

doi:10.1016/j.infrared.2023.104641

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…The colony count was expressed as colony-forming units per milliliter (CFU/mL). Soluble solids were determined using a handheld refractometer [13], pH was measured using a pH meter [14], and reducing sugar content was determined using the DNS (3,5-dinitrosalicylic) method [15].…”

Section: Determination Of Colony Count and Basic Physicochemical Indi...mentioning

confidence: 99%

Flavor Characteristics of Navel Orange Wine Fermented by Saccharomyces cerevisiae SC-125 and Angel Yeast SY

Zhang,

Ye,

Zou

et al. 2023

Fermentation

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This research utilized Jintang navel oranges as the primary raw material, and employed two distinct yeast strains, Saccharomyces cerevisiae SC-125 and Angel yeast SY, for a dual fermentation approach. Employing single-strain fermentation as the control, this study aims to ascertain the physicochemical markers, alterations in organic acids and amino acids, alongside the antioxidant properties throughout the fermentation process, all within an optimized environment. The characterization of flavor compounds in the navel orange wines subjected to diverse yeast strains and mixed fermentation was conducted using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HP-SPME/GC-MS). This method facilitated the identification of flavor compound types and concentrations. Moreover, electronic sensory systems including electronic noses and electronic eyes were harnessed to discern distinctions among various navel orange wines. Through these techniques, the research aimed to elucidate the variances induced by different yeast strains during both individual and mixed fermentation processes, shedding light on their impacts on the ultimate quality of navel orange wines.

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Section: Determination Of Colony Count and Basic Physicochemical Indi...mentioning

confidence: 99%

Flavor Characteristics of Navel Orange Wine Fermented by Saccharomyces cerevisiae SC-125 and Angel Yeast SY

Zhang,

Ye,

Zou

et al. 2023

Fermentation

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“…Zhou et al. ( Zhou, 2022 ) also investigated this relationship and discovered that SVR model offered the best predictive accuracy with an

of 1.309. Meanwhile, Benelli et al.…”

Section: Resultsmentioning

confidence: 99%

Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering

Xu,

Wei,

Cheng

et al. 2024

Front. Plant Sci.

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The maturity of kiwifruit is widely gauged by its soluble solids content (SSC), with accurate assessment being essential to guarantee the fruit’s quality. Hyperspectral imaging offers a non-destructive alternative to traditional destructive methods for SSC evaluation, though its efficacy is often hindered by the redundancy and external disturbances of spectral images. This study aims to enhance the accuracy of SSC predictions by employing feature engineering to meticulously select optimal spectral features and mitigate disturbance effects. We conducted a comprehensive investigation of four spectral pre-processing and nine spectral feature selection methods, as components of feature engineering, to determine their influence on the performance of a linear regression model based on ordinary least squares (OLS). Additionally, the stacking generalization technique was employed to amalgamate the strengths of the two most effective models derived from feature engineering. Our findings demonstrate a considerable improvement in SSC prediction accuracy post feature engineering. The most effective model, when considering both feature engineering and stacking generalization, achieved an RMSEp of 0.721, a MAPEp of 0.046, and an RPDp of 1.394 in the prediction set. The study confirms that feature engineering, especially the careful selection of spectral features, and the stacking generalization technique are instrumental in bolstering SSC prediction in kiwifruit. This advancement enhances the application of hyperspectral imaging for quality assessment, offering benefits that extend across the agricultural industry.

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“…In previous work along these lines, Zhou et al [12] examined the soluble solids and hardness of kiwifruit using a detector and achieved superior detection results. Fan et al [11] non-destructively evaluated the soluble-solid content of apples by using a portable visible-NIR (Vis-NIR) device, and Zhu et al [13] used a portable NIR spectrometer to determine the sugar content and firmness of Fuji apples.…”

Section: Introductionmentioning

confidence: 99%

Determination of Dry-Matter Content of Kiwifruit before Harvest Based on Hyperspectral Imaging

Yang,

Chen,

Qian

et al. 2024

AgriEngineering

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Determining pre-harvest fruit maturity is vital to ensure the quality of kiwifruit, and dry-matter content is an important indicator of kiwifruit ripeness. To predict the pre-harvest dry-matter content of kiwifruit continuously in real-time with high accuracy, this study uses hyperspectral data of pre-harvest Jintao kiwifruit obtained by using a hyperspectral image acquisition device. The raw data underwent whiteboard correction, spectral data extraction, spectral pre-processing, and feature-band extraction, following which the dry-matter content of the fruit was predicted by using partial least squares (PLS) regression. The feature bands extracted by the random frog method were 538.93, 671.14, 693.41, 770.61, 796.98, 813.24, 841.21, 843.29, and 856.80 nm, which improve the accuracy of the PLS method for predicting dry-matter content, with R2 = 0.92 and a root mean square error (RMSE) of 0.41% for the training set, and R2 = 0.85 and a RMSE of 0.50% for the test set. These results show that the proposed method reduces the number of required bands while maintaining the prediction accuracy, thereby demonstrating the reliability of using hyperspectral data to predict the pre-harvest dry-matter content of kiwifruit. This method can effectively guide the management of kiwifruit harvesting period, establishing a theoretical foundation for precise unmanned harvesting.

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Low-cost and handheld detector on soluble solids content and firmness of kiwifruit

Cited by 6 publications

References 26 publications

Flavor Characteristics of Navel Orange Wine Fermented by Saccharomyces cerevisiae SC-125 and Angel Yeast SY

Flavor Characteristics of Navel Orange Wine Fermented by Saccharomyces cerevisiae SC-125 and Angel Yeast SY

Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering

Determination of Dry-Matter Content of Kiwifruit before Harvest Based on Hyperspectral Imaging

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