Non-destructive evaluation of quality and ammonia content in whole and fresh-cut lettuce by computer vision system

Pace, Bernardo; Cefola, Maria; Pelo, Paolo Da; Renna, Floriana; Attolico, Giovanni

doi:10.1016/j.foodres.2014.07.037

Cited by 31 publications

(24 citation statements)

References 20 publications

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“…; Pace et al . ). Interestingly, OA treatment showed beneficial effects in reducing initial total viable count to a level similar to that obtained by hypochlorite washing.…”

Section: Discussionmentioning

confidence: 97%

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Application of Oxalic Acid to Preserve the Overall Quality of Rocket and Baby Spinach Leaves during Storage

Cefola

Pace

2015

Journal of Food Processing and Preservation

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The effect of postharvest treatment with oxalic acid (OA) on the overall quality of two leafy vegetables, rocket and baby spinach, was investigated for the first time. The leaves of rocket and baby spinach were dipped in 1 mM OA solution for 1 min and then stored for 6 days at 8C. As control, the leaves of rocket and baby spinach were immersed in hypochlorite, as currently practiced by fresh-cut companies. OA treatment significantly delayed deterioration during storage, with clear benefits for overall quality. Specifically, OA reduced the visual quality loss and yellowing of the leaves, slowing down the respiration rate, ammonia production, chlorophyll degradation and electrolyte leakage. In addition, the antioxidant activity and total phenol content were preserved by OA treatment. Finally, OA reduced the mesophilic aerobic count, with a performance similar to that of hypochlorite washing. Thus, OA treatment might be a valid method for preserving the postharvest overall quality of rocket and baby spinach. PRACTICAL APPLICATIONSThis manuscript provides new and valuable information for postharvest handling of rocket and spinach leaves. These leafy vegetables are usually washed with hypochlorite solution. As innovative postharvest treatment, oxalic acid, a natural preservative, can be applied to improve the overall quality, in response to consumer awareness of the use of chemically synthesized additives. One important issue is the control of microbial spoilage with efficacy similar to the normal procedure with sodium hypochlorite. Thus, the proposed treatment could be easily implemented in fresh-cut companies as it does not require additional facilities or investment; moreover, it gives advantages to processors and consumers in terms of shelf life extension.

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“…; Pace et al . ). Interestingly, OA treatment showed beneficial effects in reducing initial total viable count to a level similar to that obtained by hypochlorite washing.…”

Section: Discussionmentioning

confidence: 97%

“…For ammonia production, the method reported by Pace et al . () was used. In detail, 5 g of chopped sample was extracted in distilled water, and after the reaction with nitroprusside reagent and alkaline hypochlorite solution, color development was determined after incubation at 37C for 20 min, reading the absorbance at 635 nm.…”

Section: Methodsmentioning

confidence: 99%

Application of Oxalic Acid to Preserve the Overall Quality of Rocket and Baby Spinach Leaves during Storage

Cefola

Pace

2015

Journal of Food Processing and Preservation

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“…It captures images of target object by camera and then conducts further analysis of the acquired images. Because of the fast, nondestructive and objective reflection of shape, size, color, texture of the object detected, computer vision is more and more widely applied in agricultural automation field, such as maturation evaluation (Rodríguez‐Pulido et al ; Vélez‐Rivera et al ), freshness determination (Huang et al ; Pace et al ), grade sorting (Elmasry et al ; Wang, Wang, et al 2012), species distinction (Wang, Liu, et al 2012; Shafiee et al ), defect detection (Kim et al ; Hassankhani et al ), process monitoring (Hosseinpour et al ; Sampson et al ), and quantitative prediction (Matiacevich et al ; Pace et al ). Fang et al () used diameter variation of fruit to identify tomatoes with physiological diseases by genetic algorithm and artificial neural network, and the accuracy rate was up to 100%.…”

Section: Introductionmentioning

confidence: 99%

Evaluating quality of tomato during storage using fusion information of computer vision and electronic nose

Huang

Pan

Sun

et al. 2018

J Food Process Engineering

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A nondestructive method for evaluation of tomato quality was proposed to recognize the different ripening stages of tomatoes and to predict their hardness. Computer vision and electronic nose were used, and data fusion technology was used to establish the decision model of multi‐attribute information fusion. Fisher discriminant analysis (FDA) model and support vector classifier (SVC) model for tomato grading were established. Partial least squares (PLS) model and support vector regression (SVR) model were used for predicting the hardness. Comparing with the single detection technique, the combined system which fused computer vision and electronic nose, achieved better results. In the combined system: with respect to the SVC model, classification accuracy was 96.38% for the training set, 94.20% for the prediction set; while to the FDA model, classification accuracy was 93.48% for the training set, 85.51% for the prediction set; to the PLS model, the correlation coefficient of prediction (Rp) was 94.09% and the root mean square error of prediction (RMSEP) was 2.33 N; to the SVR model, the Rp was 95.14% and RMSEP was 0.03 N. It was observed that more robust and better prediction performance on tomato detection were achieved by using fusion information. Practical applications Computer vision, electronic nose, data fusion, and pattern recognition were used for the evaluation of tomato ripeness and hardness. Computer vision captured the color information of tomatoes and electronic nose responded to the smell of tomatoes. After data fusion, information became more abundant and the discrimination models also got better performance. It proved the possibility of nondestructive method for the evaluation of tomatoes quality during storage. So it is feasible to apply this method to detect tomato quality in factory.

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“…One of the most important papers in the field of color analysis for computer vision in the agriculture industry came from Pace (2014). The color of a certain region of lettuce was calculated by dividing that region's color by either white or brown.…”

Section: Color Analysismentioning

confidence: 99%

“…The analysis was done on four separate images, each capturing a certain part of the produce in question (lettuce). The authors ultimately found that color was a significant indicator of quality -both green and brown and the respective ratio of each color (Pace et al, 2014). This paper was important because it showed that color is an accurate indicator of produce's quality and the paper introduced the use of a color ratio for more precise color analysis.…”

Section: Color Analysismentioning

confidence: 99%

Using Computer Vision to Build a Predictive Model of Fruit Shelf-life

Thor¹

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Computer vision is becoming a ubiquitous technology in many industries on account of its speed, accuracy, and long-term cost efficacy. The ability of a computer vision system to quickly and efficiently make quality decisions has made computer vision a popular technology on inspection lines. However, few companies in the agriculture industry use computer vision because of the non-uniformity of sellable produce. The small number of agriculture companies that do utilize computer vision use it to extract features for size sorting or for a binary grading system: if the piece of fruit has a certain color, certain shape, and certain size, then it passes and is sold. If any of the above criteria are not met, then the fruit is discarded. This is a highly wasteful and relatively subjective process. This thesis proposes a process to undergo to use computer vision techniques to extract features of fruit and build a model to predict shelf-life based on the extracted features. Fundamentally, the existing agricultural processes that do use computer vision base their distribution decisions on current produce characteristics. The process proposed in this thesis uses current characteristics to predict future characteristics, which leads to more informed distribution decisions. By modeling future characteristics, the process proposed will allow fruit characterized as "unfit to sell" by existing standards to still be utilized (i.e. if the fruit is too ripe to ship across the country, it can still be sold locally) which decreases food waste and increases profit. The process described also removes the subjectivity present in current fruit grading systems. Further, better informed distribution decisions will save money in storage costs and excess inventory. The proposed process consists of discrete steps to follow. The first step is to choose a fruit of interest to model. Then, the first of two experiments is performed. Sugar content of a large sample of fruit are destructively measured (using a refractometer) to correlate sugar content to a color range. This step is necessary to determine the end-point of data collection because stages of ripeness are fundamentally subjective. The literature is consulted to determine "ripe" sugar content of the fruit and the first experiment is undertaken to correlate a color range that corresponds to the "ripe" sugar content. This feature range serves as the end-point of the second experiment. The second experiment is large-scale data collection of the fruit of interest, with features being recorded every day, until the fruit reaches end-of-life as determined by the first experiment. Then, computer vision is used to perform feature extraction and features are recorded over each sample fruit's lifetime. The recorded data is then analyzed with regression and other techniques to build a model of the fruit's shelf-life. The model is finally validated. This thesis uses bananas as a proof of concept of the proposed process.

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Non-destructive evaluation of quality and ammonia content in whole and fresh-cut lettuce by computer vision system

Cited by 31 publications

References 20 publications

Application of Oxalic Acid to Preserve the Overall Quality of Rocket and Baby Spinach Leaves during Storage

Application of Oxalic Acid to Preserve the Overall Quality of Rocket and Baby Spinach Leaves during Storage

Evaluating quality of tomato during storage using fusion information of computer vision and electronic nose

Using Computer Vision to Build a Predictive Model of Fruit Shelf-life

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