Detecting low-oxygen stress of stored apples using chlorophyll fluorescence imaging and histogram division

Schlie, Tim-Pascal; Dierend, Werner; Köpcke, Dirk; Rath, Thomas

doi:10.1016/j.postharvbio.2022.111901

Cited by 12 publications

(9 citation statements)

References 56 publications

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“…This non-Gaussianity was only recently highlighted empirically in [12]. In most of the recent studies using the maximum quantum yield of photosystem II as phenotyping characteristic [14,15,[17][18][19][20][21][22][24][25][26], the results are presented with average and standard deviation. Our mathematical study shows that this may not be the most appropriate approach especially when the distribution of the gray levels in F v /F m strongly deviates from Gaussianity.…”

Section: Discussionmentioning

confidence: 99%

“…This advocate for the use of box plots like in [23] to easily visualize the non-Gaussianity. The only use of average and standard deviation in [14,15,[17][18][19][20][21][22][24][25][26] was not problematic as the maximum quantum yield of photosystem II is not used as a biomarker for its absolute value but rather to differentiate different phenotypes. However, our mathematical models show that the distribution can be nonsymmetric with heavy tails.…”

Section: Discussionmentioning

confidence: 99%

“…Surprisingly, the non-Gaussianity of the chlorophyll fluorescence indices has only recently been highlighted empirically [12]. In most of the literature, Gaussianity is assumed, and therefore, one resorts to the linear associated estimators of average and standard deviation to characterize the chlorophyll fluorescence indices [13][14][15][16][17][18][19][20][21][22][23][24][25][26]. This assumption may not be an issue for the phenotyping situations considered in the literature where a measure of a biomarker is not the aim but rather a difference between a reference (genotype or control conditions) and another plant (other genotype or various stress conditions).…”

Section: Introductionmentioning

confidence: 99%

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On the Importance of Non-Gaussianity in Chlorophyll Fluorescence Imaging

et al. 2023

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We propose a mathematical study of the statistics of chlorophyll fluorescence indices. While most of the literature assumes Gaussian distributions for these indices, we demonstrate their fundamental non-Gaussian nature. Indeed, while the noise in the raw fluorescence images can be assumed as Gaussian additive, the deterministic ratio between them produces nonlinear non-Gaussian distributions. We investigate the states in which this non-Gaussianity can affect the statistical estimation when wrongly approached with linear estimators. We provide an expectation–maximization estimator adapted to the non-Gaussian distributions. We illustrate the interest of this estimator with simulations from images of chlorophyll fluorescence indices.. We demonstrate the benefits of our approach by comparison with the standard Gaussian assumption. Our expectation–maximization estimator shows low estimation errors reaching seven percent for a more pronounced deviation from Gaussianity compared to Gaussianity assumptions estimators rising to more than 70 percent estimation error. These results show the importance of considering rigorous mathematical estimation approaches in chlorophyll fluorescence indices. The application of this work could be extended to various vegetation indices also made up of a ratio of Gaussian distributions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Importance of Non-Gaussianity in Chlorophyll Fluorescence Imaging

et al. 2023

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“…With this system, six to eight fruits are measured with one sensor and the fluorescence is output as a normalized mean (Wright et al 2008). The disadvantage is that no statement can be made about an individual fruit (Schlie et al 2022). However, previous research indicated that there might be differences in fermentation behavior from fruit to fruit of the same variety and origin (Köpcke 2014;Schlie et al 2020).…”

Section: Discussionmentioning

confidence: 99%

The Impact of Fruit Ripeness on the Lower Oxygen Limit, Chlorophyll Fluorescence and Fermentation Behavior in Apples

et al. 2023

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Dynamic Controlled Atmosphere-Chlorophyll Fluorescence storage (DCA-CF) uses a fluorescence-based measurement method to detect fermentation in apples (Malus × domestica BORKH.) caused by low-oxygen levels at an early stage. In recent years, it has been observed that individual apples of the same variety and origin can exhibit different fermentation behavior when stored under completely identical conditions. The causes of the different fermentation behavior must be found in order to be able to use DCA storage optimally. This study aimed to find the causes of the different fermentation behaviors of individual apples. Our results show that fruit ripeness can affect the lower oxygen limit (LOL), especially immediately after harvest, when the starch degradation in the fruit is not yet complete. A significant increase in the LOL was observed in ‘Elstar’ (2020: 0.3 kPa, 0.6 kPa, 0.9 kPa; 2021: 0.3 kPa, 0.4 kPa, 0.6 kPa). ‘Braeburn’ also exhibited this behavior regarding the LOL at a lower level. The LOL could not be identified for some of the fruit (varying from 12.5% to 41.7% of the examined apples) previously stored in Ultra Low Oxygen (ULO) storage for 4 months. Also, the chlorophyll content in the apple skin influences the fluorescence measurement method. Within 2 weeks, the chlorophyll content in the apple skin was halved. If the chlorophyll content drops, the reliability of the fluorescence measurement also decreases. It turned out that apples with an Fv/Fm < 0.7 were unsuitable for valid LOL identification.

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“…Multiple versions of the Open FluorCam device have been developed, which usually consist of a CCD camera, four to five fixed LED panels, a computer, and, optionally, a filter wheel equipped with up to seven different emission filters. The structure of the system setup is shown in Figure 1d (Schlie et al., 2022). The Open FluorCam device has the advantage of high flexibility, enabling the measurements of samples with different sizes by adjusting the angles and distances of the LED panels and light source from the sample, as well as the position of the camera relative to the sample.…”

Section: Application Of Commercially Available Devices In the Food In...mentioning

confidence: 99%

Toward commercial applications of LED and laser‐induced fluorescence techniques for food identity, quality, and safety monitoring: A review

Peng

et al. 2023

Comp Rev Food Sci Food Safe

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The assessment of food safety and quality is a matter of paramount importance, especially considering the challenges posed by climate change. Convenient, eco‐friendly, and non‐destructive techniques have attracted extensive attention in the food industry because they can retain food safety and quality. Fluorescence radiation, the process by which fluorophore emits light upon the absorption of ultraviolet or visible light, offers the advantages of high sensitivity and selectivity. The use of excitation–emission matrix (EEM) has been extensively explored in the food industry, but on‐site detection of EEMs remain a challenge. To address this limitation, laser‐induced fluorescence (LIF) and light emitting diode‐induced fluorescence (LED‐IF) have been implemented in many cases to facilitate the transition of fluorescence measurements from the laboratory to commercial applications. This review provides an overview of the application of commercially available LIF/LED‐IF devices for non‐destructive food measurement and recent studies that focus on the development of LIF/LED‐IF devices for commercial applications. These studies were categorized into two stages: the preliminary exploration stage, which emphasizes the selection of an appropriate excitation wavelength based on the combination of EEM and chemometrics, and the pre‐application stage, where experiments were conducted on scouting with specific excitation wavelength. Although commercially available devices have emerged in many research fields, only a limited number have been reported for use in the food industry. Future studies should focus on enhancing the diversity of test samples and parameters that can be measured by a single device, exploring the application of LIF techniques for detecting low‐concentration substances in food, investigating more quantitative approaches, and developing embedded computing devices.

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Detecting low-oxygen stress of stored apples using chlorophyll fluorescence imaging and histogram division

Cited by 12 publications

References 56 publications

On the Importance of Non-Gaussianity in Chlorophyll Fluorescence Imaging

On the Importance of Non-Gaussianity in Chlorophyll Fluorescence Imaging

The Impact of Fruit Ripeness on the Lower Oxygen Limit, Chlorophyll Fluorescence and Fermentation Behavior in Apples

Toward commercial applications of LED and laser‐induced fluorescence techniques for food identity, quality, and safety monitoring: A review

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