Authors:
Thanh Tung Pham Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, H-1118, Budapest, Hungary
Faculty of Chemical and Food Technology, Ho Chi Minh City University of Technology and Education, Ho Chi Minh, 700000, Vietnam

Search for other papers by Thanh Tung Pham in
Current site
Google Scholar
PubMed
Close
,
Zinabu Hailu Siyum Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, H-1118, Budapest, Hungary

Search for other papers by Zinabu Hailu Siyum in
Current site
Google Scholar
PubMed
Close
,
Thi Thanh Nga Ha Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, H-1118, Budapest, Hungary
Faculty of Food Science and Technology, Ho Chi Minh City University of Industry and Trade, Ho Chi Minh, 700000, Vietnam

Search for other papers by Thi Thanh Nga Ha in
Current site
Google Scholar
PubMed
Close
,
Hoa Xuan Mac Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, H-1118, Budapest, Hungary
Faculty of Food Science and Technology, Ho Chi Minh City University of Industry and Trade, Ho Chi Minh, 700000, Vietnam

Search for other papers by Hoa Xuan Mac in
Current site
Google Scholar
PubMed
Close
,
Sao Mai Dam Institute of Biotechnology and Food Technology, Industrial University of Ho Chi Minh City, 700000, Vietnam

Search for other papers by Sao Mai Dam in
Current site
Google Scholar
PubMed
Close
,
Trang Ha Dieu Nguyen Institute of Biotechnology and Food Technology, Industrial University of Ho Chi Minh City, 700000, Vietnam

Search for other papers by Trang Ha Dieu Nguyen in
Current site
Google Scholar
PubMed
Close
,
Lien Le Phuong Nguyen Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, H-1118, Budapest, Hungary

Search for other papers by Lien Le Phuong Nguyen in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-5975-9906
, and
László Baranyai Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, H-1118, Budapest, Hungary

Search for other papers by László Baranyai in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-6177-7364
Open access

Abstract

The presented study investigated the effects of edible coatings with concentration of 2%, 3% and 4% of starch (w/v) on the weight loss and firmness loss of green asparagus during 4 days of storage at room temperature (26 ± 2 °C, 65–70% RH). According to the results, the coated asparagus exhibited significantly slower deterioration rate than the uncoated control samples. This was indicated by the decrease in weight loss and increase in firmness (P < 0.05). After the storage period, the samples treated with 4% starch formula retained the highest quality. Furthermore, the assessment of asparagus quality throughout the storage period involved the use of the line laser scattering technique. Extracted parameters of laser scattering signal discriminated samples with linear discriminant analysis (LDA), in which the correct recognition rate of the treated groups was 75.26% and the storage time was 70.54%. This study showed the potential of laser scattering as a rapid, non-invasive, and practical optical method for assessing the quality of asparagus during storage.

Abstract

The presented study investigated the effects of edible coatings with concentration of 2%, 3% and 4% of starch (w/v) on the weight loss and firmness loss of green asparagus during 4 days of storage at room temperature (26 ± 2 °C, 65–70% RH). According to the results, the coated asparagus exhibited significantly slower deterioration rate than the uncoated control samples. This was indicated by the decrease in weight loss and increase in firmness (P < 0.05). After the storage period, the samples treated with 4% starch formula retained the highest quality. Furthermore, the assessment of asparagus quality throughout the storage period involved the use of the line laser scattering technique. Extracted parameters of laser scattering signal discriminated samples with linear discriminant analysis (LDA), in which the correct recognition rate of the treated groups was 75.26% and the storage time was 70.54%. This study showed the potential of laser scattering as a rapid, non-invasive, and practical optical method for assessing the quality of asparagus during storage.

Introduction

Green asparagus (Asparagus officinalis L.) is a valuable and highly demanded fresh vegetable, well-known for distinct taste, and also holding significant economic value in the markets. However, its high respiration rate leads to a short shelf-life after harvesting. This is caused by various biochemical reactions, resulting in a decline in quality that manifests as increased weight loss and hardness (You et al., 2021). Therefore, it is a challenge to ensure extended shelf-life of this vegetable and maintain its quality for commercialization (Zsom et al., 2016). Previous studies have investigated the effects of modified atmosphere packaging (MAP) and hypobaric pressure on the shelf-life of green asparagus (An et al., 2007; Albanese et al., 2007; Wei and Ye, 2011). However, these results indicate that more methods need to be investigated to improve the quality of green asparagus during storage.

The application of edible coatings to extend the shelf-life of fruits and vegetables has attracted attention in recent years. Edible coatings create a gas barrier equivalent to controlled atmosphere storage (Pham et al., 2022). Furthermore, the use of biological packaging as an alternative is also receiving attention due to environmental concerns (Nguyen et al., 2018). However, edible coatings must meet certain requirements to be used at a commercial level (Panahirad et al., 2020). Cassava starch is used as a coating for vegetables due to its availability, low cost and biodegradability. Besides, cassava starch coatings can inhibit gas migration due to their hydrogen bond network structure. These coatings are typically colorless and odorless, and are generally considered safe and nontoxic, making them a popular choice for use as edible coatings. Furthermore, their mechanical properties can be modified for desired improvement (Pham et al., 2023).

Overall, the firmness of green asparagus is an important factor influencing consumer acceptance. Sensory and destructive techniques are commonly used to evaluate the firmness of asparagus. However, this method has a limitation in that sampling is limited to only a few samples in a batch (Sanz et al., 2009; Bodor et al., 2018). To overcome these limitations, optical methods are applied to monitor the physicochemical changes of products during harvest and post-harvest (Zsom et al., 2018; Nguyen et al., 2020; Zsom-Muha et al., 2021). The effectiveness of near-infrared reflectance (NIR) spectroscopy as a non-invasive method for food quality monitoring has been demonstrated (Felföldi et al., 2017; Baranyai, 2011). It offers advantages such as fast speed, measurement reproducibility, and no sample preparation required. NIR is a tool that predicts the chemical properties of foods within a specific wavelength range. However, it does not provide quantitative data on light dispersion in the sample. In NIR techniques, scattering effects are considered noise and are removed or resolved during spectral analysis (Romano et al., 2011). In contrast to NIR, a potential method of increasing interest involves the absorption of photons and diffuse scattered light. The surface scattering properties of food are the result of the interaction between its absorption and scattering. Absorption provides detailed information about chemical constituents while scattering reveals characteristics of cell or tissue structures (Zsom et al., 2014). Many studies have been conducted to explore the potential of light scattering properties in predicting food quality (Zsom et al., 2021; Hencz et al., 2022). However, there is a lack of research evaluating the influence of light scattering properties in predicting or grading asparagus quality.

Therefore, this study was conducted to evaluate the effect of edible coatings with different starch concentrations on quality parameters including weight loss and firmness during storage at room temperature. In addition, laser scattering technique was applied to classify asparagus quality during storage. These classifications were based on the analysis of light propagation characteristics.

Materials and methods

Materials

A total of 200 fresh, uniform, and washed green asparagus were obtained directly from the grower in Csengőd, Hungary. The cassava starch powder, with the minimum purity of 99% on dry basis, was purchased from Hunorganic Ltd., Hungary. Gelatin with a purity exceeding 99%, sorbitol with a purity exceeding 99% and glycerol with a purity over 99% were each bought from different suppliers: Szilasfood Ltd., Hungary; Parma Produkt Ltd., Hungary; and Budai Szent Klara Pharmacy in Hungary, respectively.

Table 1 presents formula of coating that was applied to the different sample groups. Cassava starch was heated to 95 °C and held at this temperature for 30 min to reach complete gelatinization. Gelatin was hydrated for 1 h and then heated to 70 °C, and kept on this temperature for 30 min. The materials were blended in the prescribed sequence, starting with cassava starch, followed by gelatin, sorbitol, and glycerol. Coating of the asparagus was performed by immersing them in the prepared solutions for 15 s at room temperature. Following the coating step, the asparagus were left to gradually dry for 1 h at ambient temperature. After that, the coated samples were placed on plastic trays and stored at room temperature.

Table 1.

The constituents included in different coating formulations

Sample groupComponents (%, w/v)
Cassava starchGelatinSorbitolGlycerol
Control0000
S220.530.5
S330.530.5
S440.530.5

Weight loss

The weight loss of asparagus during storage was assessed by periodically measuring the weight using an electronic weighing device (Kern PFB, Kern & Sohn Gmbh, Balingen-Frommern, Germany) with the accuracy of 1 mg. The change in weight was monitored at different intervals, and the results were expressed as the percentage of initial weight (0 d).

Firmness

Asparagus firmness was measured by TA.XTplus (Stable Micro Systems, Godalming, UK) as described by Tzoumaki et al. (2009). A cutting test was performed using a TA-TPB probe to make precise cuts at the designated positions on the asparagus. The measurements were performed using a probe with constant speed at 1.0 mm s−1. The Texture Expert software (version 1.0) was used to record the force, penetration depth, and time data. The firmness value was defined as the highest force applied during the cutting test.

Laser scattering

A laser system was developed for the assessment of green asparagus quality throughout storage period. The setup included a dark box with a single line laser module and a digital camera (SONY, DSC-W800, Tokyo, Japan). A single line laser module (635 nm) with the power of 1 mW and the line thickness of 1 mm was used to illuminate the asparagus samples. For the experiment, the lens of the camera and specimen were situated 27 cm apart, while the line laser was adjusted to the angle of 20°. In this study, the general workflow of image processing performed by Scilab is shown in Fig. 1. The intensity profile of the images was analyzed using Scilab software (version 6.1.1) to extract the signal width at 50% (full width half maximum, FWHM), 75% (D75) and 25% (D25).

Fig. 1.
Fig. 1.

Workflow of image processing: a) Image acquisition, b) Features extraction

Citation: Progress in Agricultural Engineering Sciences 19, S1; 10.1556/446.2023.00083

Statistical analysis

Each measurement was made on 10 pieces of green asparagus and was repeated 3 times. The analysis of the data was performed with the SPSS version 19 (IBM Corp., Armonk, N.Y., USA). Two-way ANOVA was conducted to evaluate the data. The data was represented in figures displaying the average value ± standard deviation (P < 0.05).

Results and discussion

Weight loss

Weight loss is a critical parameter of asparagus during storage due to its direct correlation with negative impact on appearance, resulting in economic losses (Sanz et al., 2009). As illustrated in Fig. 2, weight loss in asparagus gradually increased during storage. This was a result of transpiration, driven by variations in pressure between the asparagus and the surrounding environment, leading to water loss (Tzoumaki et al., 2009). The findings indicated that applying these coatings considerably decreased weight loss by limiting gas permeability on the asparagus surface, thereby decreasing respiration rate and weight loss. After 4 d of storage, the control group exhibited a 48.9% reduction in weight, while the coated asparagus resulted in weight losses ranging from 23.84% to 32.64%. Among the coated samples, S4 demonstrated the highest effectiveness in reducing weight loss. The results suggest that increased amount of starch in the coating formula resulted in increased coating thickness, which improved its effectiveness during storage (Pham et al., 2022).

Fig. 2.
Fig. 2.

Asparagus weight loss during storage. Capital letters are time comparisons (P < 0.05) while small letters are treatment comparisons (P < 0.05)

Citation: Progress in Agricultural Engineering Sciences 19, S1; 10.1556/446.2023.00083

Firmness

Asparagus quality is evaluated based on its firmness, as it is an important factor for consumers (Lau et al., 2000). It was observed that the firmness of green asparagus increased during storage (Fig. 3). The increase in firmness is probably associated with the increase of crude fiber, specifically lignin, and the enzyme activity of phenylalanine ammonia-lyase (Lau et al., 2000). The data showed that the coated asparagus had lower firmness compared to the control sample. The application of the coating aimed to alter the gas composition within the asparagus by decreasing oxygen while increasing carbon dioxide levels. This modification slowed down the biochemical reactions responsible for firmness changes. These results were consistent with the findings of Tzoumaki et al. (2009) and Qiu et al. (2013). The S4 coating formula was the most effective in slowing the increase in asparagus firmness among the treated samples. The findings indicated that the amount of starch in the coating formula had a significant impact on asparagus firmness. This result was because of the rise in polymer components and the overall dissolved substances within the coating layer, despite its volume remaining constant (Pham et al., 2022).

Fig. 3.
Fig. 3.

Asparagus firmness during storage. Capital letters are time comparisons (P < 0.05) while small letters are treatment comparisons (P < 0.05)

Citation: Progress in Agricultural Engineering Sciences 19, S1; 10.1556/446.2023.00083

Laser scattering

Laser scattering has proved to be effective in quality evaluation of solid foods like fruits and vegetables (Baranyai and Zude, 2008). The data presented in Table 2 indicated that changes in the internal quality of asparagus had significant impact on the sensitivity of the signal shape. Compared with coated samples, the control sample exhibited lower FWHM values in the intensity profile. Besides, a decrease in scattering around the incident point was observed as storage time increased. The variations detected in the data extracted from scattering images throughout storage could be attributed to the changes in weight loss and firmness. As the moisture content decreased, the replacement of air in the space between cells led to increase in material density (Romano et al., 2011). Hence, the backscattering region was reduced due to the increase of absorption and the refraction reduction. Firmer vegetables were difficult for light penetration into the tissue, resulting in a sharper gradient (Hashim et al., 2018). This was due to the reduction in distance of gradient around the halo. Besides, a decline in chlorophyll content could lead to the alterations in the backscattering at 635 nm (Mozaffari et al., 2022). Furthermore, ANOVA results indicated that storage time had the primary effect on the intensity profile, followed by the treatment factor, and no interaction amongst factors was observed (Table 3).

Table 2.

The parameters extracted from the light scattering signal

Time/treatment1234
FWHMD75D25FWHMD75D25FWHMD75D25FWHMD75D25
Control37.2322.5751.0733.8716.7747.6328.7317.0739.2329.7017.5740.39
S237.2020.1751.6035.7319.4349.9031.4719.1342.3329.3917.7838.91
S338.2023.0354.6735.0319.2748.3032.3019.6745.5029.8718.0040.60
S437.6320.3354.0336.1018.9351.5332.9319.0744.3330.9318.2341.43
Table 3.

F-values from ANOVA analysis of the parameters extracted from the light scattering signal

ParameterTimeTreatmentInteraction
FWHM5.288***3.894**0.861
D752.005***2.602*1.522
D257.403***4.649**0.812

***: P < 0.001, **: P < 0.01, *: P < 0.05.

Furthermore, the effectiveness of laser scattering in classifying the asparagus quality was assessed through LDA. The data were classified into groups according to storage time and the treatment. The finding indicated that the precision of categorization was good for both the time (70.54%) and treatment group (75.26%). In previous research, it was found that the accuracy of categorizing stored bananas exceeded 90% (Zulkifli et al., 2019). Yang et al. (2021) reported that the classification accuracy for “Hayward” kiwifruit was lower (49–61%) in comparison to the findings from this research.

Conclusions

The study examined the impact of cassava starch-based coating with varying starch content on the quality of fresh green asparagus stored at room temperature. Quality parameters, including weight loss and firmness, were evaluated to assess the efficacy of coating. The findings revealed that the coating with 4% starch content was the most effective in maintaining the asparagus quality and extending its storage duration. Further research is required to explore and optimize additional methods that can enhance the shelf-life of asparagus in combination with coating. The laser scattering technique distinguished the quality classes of asparagus during storage with correct classification rate above 70%. The research indicated the potential of laser scattering technique as a non-destructive method for monitoring the asparagus quality.

Acknowledgements

We would like to thank the Doctoral School of Food Science, Hungarian University of Agriculture and Life Sciences. Additionally, the authors extend their gratitude to HCMC University of Technology and Education for their support.

References

  • Albanese, D., Russo, L., Cinquanta, L., Brasiello, A., and Di Matteo, M. (2007). Physical and chemical changes in minimally processed green asparagus during cold-storage. Food Chemistry, 101(1): 274280. https://doi.org/10.1016/j.foodchem.2006.01.048.

    • Search Google Scholar
    • Export Citation
  • An, J., Zhang, M., and Lu, Q. (2007). Changes in some quality indexes in fresh-cut green asparagus pretreated with aqueous ozone and subsequent modified atmosphere packaging. Journal of Food Engineering78(1): 340344. https://doi.org/10.1016/j.jfoodeng.2005.10.001.

    • Search Google Scholar
    • Export Citation
  • Baranyai, L. (2011). Estimation of optical properties in postharvest and processing technology. InTech, pp. 829840. https://doi.org/10.5772/15081.

    • Search Google Scholar
    • Export Citation
  • Baranyai, L. and Zude, M. (2008). Analysis of laser light migration in apple tissue by Monte Carlo simulation. Progress in Agricultural Engineering Sciences, 4(1): 4559. https://doi.org/10.1556/progress.4.2008.3.

    • Search Google Scholar
    • Export Citation
  • Bodor, Z., Koncz, F.A., Rashed, M.S., Kaszab, T., Gillay, Z., Benedek, C., and Kovacs, Z. (2018). Application of near infrared spectroscopy and classical analytical methods for the evaluation of Hungarian honey. Progress in Agricultural Engineering Sciences, 14(s1): 1123. https://doi.org/10.1556/446.14.2018.s1.2.

    • Search Google Scholar
    • Export Citation
  • Felföldi, J., Kertész, I., Nagy, D., and Zsom-Muha, V. (2017). Non-destructive impact method for quality assessment of horticultural products. Progress in Agricultural Engineering Sciences, 13(1): 6993. https://doi.org/10.1556/446.13.2017.5.

    • Search Google Scholar
    • Export Citation
  • Hashim, N., Adebayo, S.E., Abdan, K., and Hanafi, M. (2018). Comparative study of transform-based image texture analysis for the evaluation of banana quality using an optical backscattering system. Postharvest Biology and Technology, 135: 3850. https://doi.org/10.1016/j.postharvbio.2017.08.021.

    • Search Google Scholar
    • Export Citation
  • Hencz, A., Nguyen, L.L.P., Baranyai, L., and Albanese, D. (2022). Assessment of wine adulteration using near infrared spectroscopy and laser backscattering imaging. Processes, 10(1): 95. https://doi.org/10.3390/pr10010095.

    • Search Google Scholar
    • Export Citation
  • Lau, M.H., Tang, J., and Swanson, B.G. (2000). Kinetics of textural and color changes in green asparagus during thermal treatments. Journal of Food Engineering, 45(4): 231236. https://doi.org/10.1016/S0260-8774(00)00069-8.

    • Search Google Scholar
    • Export Citation
  • Le Nguyen, L.P., Visy, A., Baranyai, L., Friedrich, L., and Mahajan, P.V. (2020). Application of hue spectra fingerprinting during cold storage and shelf-life of packaged sweet cherry. Journal of Food Measurement and Characterization, 14: 26892702. https://doi.org/10.1007/s11694-020-00515-z.

    • Search Google Scholar
    • Export Citation
  • Mozaffari, M., Sadeghi, S., and Asefi, N. (2022). Prediction of the quality properties and maturity of apricot by laser light backscattering imaging. Postharvest Biology and Technology, 186: 111842. https://doi.org/10.1016/j.postharvbio.2022.111842.

    • Search Google Scholar
    • Export Citation
  • Panahirad, S., Naghshiband-Hassani, R., Bergin, S., Katam, R., and Mahna, N. (2020). Improvement of postharvest quality of plum (Prunus domestica L.) using polysaccharide-based edible coatings. Plants, 9(9): 1148. https://doi.org/10.3390/plants9091148.

    • Search Google Scholar
    • Export Citation
  • Pham, T.T., Nguyen, L.L.P., Dam, M.S., and Baranyai, L. (2023). Application of edible coating in extension of fruit shelf life. AgriEngineering, 5(1): 520536. https://doi.org/10.3390/agriengineering5010034.

    • Search Google Scholar
    • Export Citation
  • Pham, T.T., Nguyen, L.P.L., Baranyai, L., Nguyen, T.L., and Trinh, K.S. (2022). Effect of electrolyzed cassava starch-gelatin coating on biochemical properties and ripening of banana (Musa acuminata L.) fruits. Polish Journal of Food and Nutrition Sciences, 72(3): 263272. https://doi.org/10.31883/pjfns/152667.

    • Search Google Scholar
    • Export Citation
  • Phuong Le Nguyen, L., Horváth, V., Dam, M.S., Hitka, G., Zsom, T., and Kókai, Z. (2018). Effect of 1-MCP, ethylene absorber and ozone on melon quality during storage. Progress in Agricultural Engineering Sciences, 14(s1): 101110. https://doi.org/10.1556/446.14.2018.s1.10.

    • Search Google Scholar
    • Export Citation
  • Qiu, M., Jiang, H., Ren, G., Huang, J., and Wang, X. (2013). Effect of chitosan coatings on postharvest green asparagus quality. Carbohydrate Polymers, 92(2): 20272032. https://doi.org/10.1016/j.carbpol.2012.11.070.

    • Search Google Scholar
    • Export Citation
  • Romano, G., Nagle, M., Argyropoulos, D., and Muller, J. (2011). Laser light backscattering to monitor moisture content, soluble solid content, and hardness of apple tissue during drying. Journal of Food Engineering, 104: 657662. https://doi.org/10.1016/j.jfoodeng.2011.01.026.

    • Search Google Scholar
    • Export Citation
  • Sanz, S., Olarte, C., Ayala, F., and Echávarri, J.F. (2009). Evolution of quality characteristics of minimally processed asparagus during storage in different lighting conditions. Journal of food science, 74(6): S296S302. https://doi.org/10.1111/j.1750-3841.2009.01239.x.

    • Search Google Scholar
    • Export Citation
  • Tzoumaki, M.V., Biliaderis, C.G., and Vasilakakis, M. (2009). Impact of edible coatings and packaging on quality of white asparagus (Asparagus officinalis, L.) during cold storage. Food Chemistry, 117(1): 5563. https://doi.org/10.1016/j.foodchem.2009.03.076.

    • Search Google Scholar
    • Export Citation
  • Wei, Y. and Ye, X. (2011). Effect of 6‐benzylaminopurine combined with ultrasound as pre‐treatment on quality and enzyme activity of green asparagus. Journal of Food Processing and Preservation, 35(5): 587595. https://doi.org/10.1111/j.1745-4549.2010.00504.x.

    • Search Google Scholar
    • Export Citation
  • Yang, Z., Li, M., East, A.R., and Zude-Sasse, M. (2021). Application of absorption and scattering properties obtained through image pre-classification method using a laser backscattering imaging system to detect kiwifruit chilling injury. Foods, 10(7): 1446. https://doi.org/10.3390/foods10071446.

    • Search Google Scholar
    • Export Citation
  • You, Y., Li, M., Kang, T., Ko, Y., Kim, S., Lee, S.H., and Jun, S. (2021). Application of supercooling for the enhanced shelf life of Asparagus (Asparagus officinalis L.). Foods, 10(10): 2361. https://doi.org/10.3390/foods10102361.

    • Search Google Scholar
    • Export Citation
  • Zsom, T., Strohmayer, E., Phuong Le Nguyen, L., Hitka, G., and Zsom-Muha, V. (2018). Chilling injury investigation by non-destructive measuring methods during banana cold storage. Progress in Agricultural Engineering Sciences, 14(s1): 147158. https://doi.org/10.1556/446.14.2018.s1.14.

    • Search Google Scholar
    • Export Citation
  • Zsom, T., Zsom-Muha, V., Dénes, D.L., Baranyai, L., and Felföldi, J. (2016). Quality changes of pear during shelf-life. Progress in Agricultural Engineering Sciences, 12(1): 81106. https://doi.org/10.1556/446.12.2016.5.

    • Search Google Scholar
    • Export Citation
  • Zsom, T., Zsom-Muha, V., Dénes, D., Hitka, G., Nguyen, L., and Felföldi, J. (2014). Non-destructive postharvest quality monitoring of different pear and sweet pepper cultivars. Acta Alimentaria, 43(Supplement-1): 206214. https://doi.org/10.1556/aalim.43.2014.suppl.28.

    • Search Google Scholar
    • Export Citation
  • Zsom, T., Zsom-Muha, V., Le Nguyen, L.P., Nagy, D., Hitka, G., Polgári, P., and Baranyai, L. (2021). Nondestructive detection of low temperature induced stress on postharvest quality of kápia type sweet pepper. Progress in Agricultural Engineering Sciences, 16(S2): 173186. https://doi.org/10.1556/446.2020.20019.

    • Search Google Scholar
    • Export Citation
  • Zsom-Muha, V., Nguyen, L.L.P., Baranyai, L., Hitka, G., Horváth-Mezőfi, Z., Szabó, G., and Zsom, T. (2021). An attempt to the nondestructive investigation of photo-induced potato postharvest quality degradation–Preliminary results. Progress in Agricultural Engineering Sciences, 17(S1): 99109. https://doi.org/10.1556/446.2021.30012.

    • Search Google Scholar
    • Export Citation
  • Zulkifli, N., Hashim, N., Abdan, K., and Hanafi, M. (2019). Application of laser-induced backscattering imaging for predicting and classifying ripening stages of “Berangan” bananas. Computers and Electronics in Agriculture, 160: 100107. https://doi.org/10.1016/j.compag.2019.02.031.

    • Search Google Scholar
    • Export Citation
  • Albanese, D., Russo, L., Cinquanta, L., Brasiello, A., and Di Matteo, M. (2007). Physical and chemical changes in minimally processed green asparagus during cold-storage. Food Chemistry, 101(1): 274280. https://doi.org/10.1016/j.foodchem.2006.01.048.

    • Search Google Scholar
    • Export Citation
  • An, J., Zhang, M., and Lu, Q. (2007). Changes in some quality indexes in fresh-cut green asparagus pretreated with aqueous ozone and subsequent modified atmosphere packaging. Journal of Food Engineering78(1): 340344. https://doi.org/10.1016/j.jfoodeng.2005.10.001.

    • Search Google Scholar
    • Export Citation
  • Baranyai, L. (2011). Estimation of optical properties in postharvest and processing technology. InTech, pp. 829840. https://doi.org/10.5772/15081.

    • Search Google Scholar
    • Export Citation
  • Baranyai, L. and Zude, M. (2008). Analysis of laser light migration in apple tissue by Monte Carlo simulation. Progress in Agricultural Engineering Sciences, 4(1): 4559. https://doi.org/10.1556/progress.4.2008.3.

    • Search Google Scholar
    • Export Citation
  • Bodor, Z., Koncz, F.A., Rashed, M.S., Kaszab, T., Gillay, Z., Benedek, C., and Kovacs, Z. (2018). Application of near infrared spectroscopy and classical analytical methods for the evaluation of Hungarian honey. Progress in Agricultural Engineering Sciences, 14(s1): 1123. https://doi.org/10.1556/446.14.2018.s1.2.

    • Search Google Scholar
    • Export Citation
  • Felföldi, J., Kertész, I., Nagy, D., and Zsom-Muha, V. (2017). Non-destructive impact method for quality assessment of horticultural products. Progress in Agricultural Engineering Sciences, 13(1): 6993. https://doi.org/10.1556/446.13.2017.5.

    • Search Google Scholar
    • Export Citation
  • Hashim, N., Adebayo, S.E., Abdan, K., and Hanafi, M. (2018). Comparative study of transform-based image texture analysis for the evaluation of banana quality using an optical backscattering system. Postharvest Biology and Technology, 135: 3850. https://doi.org/10.1016/j.postharvbio.2017.08.021.

    • Search Google Scholar
    • Export Citation
  • Hencz, A., Nguyen, L.L.P., Baranyai, L., and Albanese, D. (2022). Assessment of wine adulteration using near infrared spectroscopy and laser backscattering imaging. Processes, 10(1): 95. https://doi.org/10.3390/pr10010095.

    • Search Google Scholar
    • Export Citation
  • Lau, M.H., Tang, J., and Swanson, B.G. (2000). Kinetics of textural and color changes in green asparagus during thermal treatments. Journal of Food Engineering, 45(4): 231236. https://doi.org/10.1016/S0260-8774(00)00069-8.

    • Search Google Scholar
    • Export Citation
  • Le Nguyen, L.P., Visy, A., Baranyai, L., Friedrich, L., and Mahajan, P.V. (2020). Application of hue spectra fingerprinting during cold storage and shelf-life of packaged sweet cherry. Journal of Food Measurement and Characterization, 14: 26892702. https://doi.org/10.1007/s11694-020-00515-z.

    • Search Google Scholar
    • Export Citation
  • Mozaffari, M., Sadeghi, S., and Asefi, N. (2022). Prediction of the quality properties and maturity of apricot by laser light backscattering imaging. Postharvest Biology and Technology, 186: 111842. https://doi.org/10.1016/j.postharvbio.2022.111842.

    • Search Google Scholar
    • Export Citation
  • Panahirad, S., Naghshiband-Hassani, R., Bergin, S., Katam, R., and Mahna, N. (2020). Improvement of postharvest quality of plum (Prunus domestica L.) using polysaccharide-based edible coatings. Plants, 9(9): 1148. https://doi.org/10.3390/plants9091148.

    • Search Google Scholar
    • Export Citation
  • Pham, T.T., Nguyen, L.L.P., Dam, M.S., and Baranyai, L. (2023). Application of edible coating in extension of fruit shelf life. AgriEngineering, 5(1): 520536. https://doi.org/10.3390/agriengineering5010034.

    • Search Google Scholar
    • Export Citation
  • Pham, T.T., Nguyen, L.P.L., Baranyai, L., Nguyen, T.L., and Trinh, K.S. (2022). Effect of electrolyzed cassava starch-gelatin coating on biochemical properties and ripening of banana (Musa acuminata L.) fruits. Polish Journal of Food and Nutrition Sciences, 72(3): 263272. https://doi.org/10.31883/pjfns/152667.

    • Search Google Scholar
    • Export Citation
  • Phuong Le Nguyen, L., Horváth, V., Dam, M.S., Hitka, G., Zsom, T., and Kókai, Z. (2018). Effect of 1-MCP, ethylene absorber and ozone on melon quality during storage. Progress in Agricultural Engineering Sciences, 14(s1): 101110. https://doi.org/10.1556/446.14.2018.s1.10.

    • Search Google Scholar
    • Export Citation
  • Qiu, M., Jiang, H., Ren, G., Huang, J., and Wang, X. (2013). Effect of chitosan coatings on postharvest green asparagus quality. Carbohydrate Polymers, 92(2): 20272032. https://doi.org/10.1016/j.carbpol.2012.11.070.

    • Search Google Scholar
    • Export Citation
  • Romano, G., Nagle, M., Argyropoulos, D., and Muller, J. (2011). Laser light backscattering to monitor moisture content, soluble solid content, and hardness of apple tissue during drying. Journal of Food Engineering, 104: 657662. https://doi.org/10.1016/j.jfoodeng.2011.01.026.

    • Search Google Scholar
    • Export Citation
  • Sanz, S., Olarte, C., Ayala, F., and Echávarri, J.F. (2009). Evolution of quality characteristics of minimally processed asparagus during storage in different lighting conditions. Journal of food science, 74(6): S296S302. https://doi.org/10.1111/j.1750-3841.2009.01239.x.

    • Search Google Scholar
    • Export Citation
  • Tzoumaki, M.V., Biliaderis, C.G., and Vasilakakis, M. (2009). Impact of edible coatings and packaging on quality of white asparagus (Asparagus officinalis, L.) during cold storage. Food Chemistry, 117(1): 5563. https://doi.org/10.1016/j.foodchem.2009.03.076.

    • Search Google Scholar
    • Export Citation
  • Wei, Y. and Ye, X. (2011). Effect of 6‐benzylaminopurine combined with ultrasound as pre‐treatment on quality and enzyme activity of green asparagus. Journal of Food Processing and Preservation, 35(5): 587595. https://doi.org/10.1111/j.1745-4549.2010.00504.x.

    • Search Google Scholar
    • Export Citation
  • Yang, Z., Li, M., East, A.R., and Zude-Sasse, M. (2021). Application of absorption and scattering properties obtained through image pre-classification method using a laser backscattering imaging system to detect kiwifruit chilling injury. Foods, 10(7): 1446. https://doi.org/10.3390/foods10071446.

    • Search Google Scholar
    • Export Citation
  • You, Y., Li, M., Kang, T., Ko, Y., Kim, S., Lee, S.H., and Jun, S. (2021). Application of supercooling for the enhanced shelf life of Asparagus (Asparagus officinalis L.). Foods, 10(10): 2361. https://doi.org/10.3390/foods10102361.

    • Search Google Scholar
    • Export Citation
  • Zsom, T., Strohmayer, E., Phuong Le Nguyen, L., Hitka, G., and Zsom-Muha, V. (2018). Chilling injury investigation by non-destructive measuring methods during banana cold storage. Progress in Agricultural Engineering Sciences, 14(s1): 147158. https://doi.org/10.1556/446.14.2018.s1.14.

    • Search Google Scholar
    • Export Citation
  • Zsom, T., Zsom-Muha, V., Dénes, D.L., Baranyai, L., and Felföldi, J. (2016). Quality changes of pear during shelf-life. Progress in Agricultural Engineering Sciences, 12(1): 81106. https://doi.org/10.1556/446.12.2016.5.

    • Search Google Scholar
    • Export Citation
  • Zsom, T., Zsom-Muha, V., Dénes, D., Hitka, G., Nguyen, L., and Felföldi, J. (2014). Non-destructive postharvest quality monitoring of different pear and sweet pepper cultivars. Acta Alimentaria, 43(Supplement-1): 206214. https://doi.org/10.1556/aalim.43.2014.suppl.28.

    • Search Google Scholar
    • Export Citation
  • Zsom, T., Zsom-Muha, V., Le Nguyen, L.P., Nagy, D., Hitka, G., Polgári, P., and Baranyai, L. (2021). Nondestructive detection of low temperature induced stress on postharvest quality of kápia type sweet pepper. Progress in Agricultural Engineering Sciences, 16(S2): 173186. https://doi.org/10.1556/446.2020.20019.

    • Search Google Scholar
    • Export Citation
  • Zsom-Muha, V., Nguyen, L.L.P., Baranyai, L., Hitka, G., Horváth-Mezőfi, Z., Szabó, G., and Zsom, T. (2021). An attempt to the nondestructive investigation of photo-induced potato postharvest quality degradation–Preliminary results. Progress in Agricultural Engineering Sciences, 17(S1): 99109. https://doi.org/10.1556/446.2021.30012.

    • Search Google Scholar
    • Export Citation
  • Zulkifli, N., Hashim, N., Abdan, K., and Hanafi, M. (2019). Application of laser-induced backscattering imaging for predicting and classifying ripening stages of “Berangan” bananas. Computers and Electronics in Agriculture, 160: 100107. https://doi.org/10.1016/j.compag.2019.02.031.

    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand

 

 

The author instruction is available in PDF.
Please, download the file from HERE.

 

 

Senior editors

Editor(s)-in-Chief: Felföldi, József

Chair of the Editorial Board Szendrő, Péter

Editorial Board

  • Beke, János (Szent István University, Faculty of Mechanical Engineerin, Gödöllő – Hungary)
  • Fenyvesi, László (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Szendrő, Péter (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Felföldi, József (Szent István University, Faculty of Food Science, Budapest – Hungary)

 

Advisory Board

  • De Baerdemaeker, Josse (KU Leuven, Faculty of Bioscience Engineering, Leuven - Belgium)
  • Funk, David B. (United States Department of Agriculture | USDA • Grain Inspection, Packers and Stockyards Administration (GIPSA), Kansas City – USA
  • Geyer, Martin (Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Horticultural Engineering, Potsdam - Germany)
  • Janik, József (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Kutzbach, Heinz D. (Institut für Agrartechnik, Fg. Grundlagen der Agrartechnik, Universität Hohenheim – Germany)
  • Mizrach, Amos (Institute of Agricultural Engineering. ARO, the Volcani Center, Bet Dagan – Israel)
  • Neményi, Miklós (Széchenyi University, Department of Biosystems and Food Engineering, Győr – Hungary)
  • Schulze-Lammers, Peter (University of Bonn, Institute of Agricultural Engineering (ILT), Bonn – Germany)
  • Sitkei, György (University of Sopron, Institute of Wood Engineering, Sopron – Hungary)
  • Sun, Da-Wen (University College Dublin, School of Biosystems and Food Engineering, Agriculture and Food Science, Dublin – Ireland)
  • Tóth, László (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)

Prof. Felföldi, József
Institute: MATE - Hungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology, Department of Measurements and Process Control
Address: 1118 Budapest Somlói út 14-16
E-mail: felfoldi.jozsef@uni-mate.hu

Indexing and Abstracting Services:

  • CABI
  • ERIH PLUS
  • SCOPUS

2022  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
9
Scimago
Journal Rank
0.191
Scimago Quartile Score

Environmental Engineering (Q4)
Industrial Manufacturing Engineering (Q3)
Mechanical Engineering (Q3)

Scopus  
Scopus
Cite Score
1.1
Scopus
CIte Score Rank
General Agricultural and Biological Sciences 141/213 (34th PCTL)
Agricultural and Biological Sciences 104/147 (29th PCTL)
Industrial and Manufacturing Engineering 261/355 (26th PCTL)
Mechanical Engineering 494/631 (21st PCTL)
Environmental Engineering 145/184 (21st PCTL)
 
Scopus
SNIP
0.222

2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
8
Scimago
Journal Rank
0,141
Scimago Quartile Score Environmental Engineering (Q4)
Industrial and Manufacturing Engineering (Q4)
Mechanical Engineering (Q4)
Scopus  
Scopus
Cite Score
0,8
Scopus
CIte Score Rank
Industrial and Manufacturing Engineering 261/338 (Q4)
Environmental Engineering 138/173 (Q4)
Mechanical Engineering 495/601 (Q4)
Scopus
SNIP
0,381

2020  
Scimago
H-index
8
Scimago
Journal Rank
0,197
Scimago
Quartile Score
Environmental Engineering Q4
Industrial and Manufacturing Engineering Q3
Mechanical Engineering Q4
Scopus
Cite Score
33/69=0,5
Scopus
Cite Score Rank
Environmental Engineering 126/146 (Q4)
Industrial and Manufacturing Engineering 269/336 (Q3)
Mechanical Engineering 512/596 (Q4)
Scopus
SNIP
0,211
Scopus
Cites
53
Scopus
Documents
41
Days from submission to acceptance 122
Days from acceptance to publication 40
Acceptance rate 86%

 

2019  
Scimago
H-index
6
Scimago
Journal Rank
0,123
Scimago
Quartile Score
Environmental Engineering Q4
Industrial and Manufacturing Engineering Q4
Mechanical Engineering Q4
Scopus
Cite Score
18/33=0,5
Scopus
Cite Score Rank
Environmental Engineering 108/132 (Q4)
Industrial and Manufacturing Engineering 242/340 (Q3)
Mechanical Engineering 481/585 (Q4)
Scopus
SNIP
0,211
Scopus
Cites
13
Scopus
Documents
5

 

Progress in Agricultural Engineering Sciences
Publication Model Hybrid
Submission Fee none
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Article Processing Charge 900 EUR/article
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2023 Online subsscription: 152 EUR / 185 USD
Print + online subscription: 177 EUR / 215 USD
Subscription Information Online subscribers are entitled access to all back issues published by Akadémiai Kiadó for each title for the duration of the subscription, as well as Online First content for the subscribed content.
Purchase per Title Individual articles can be purchased at the prices indicated.

Progress in Agricultural Engineering Sciences
Language English
Size B5
Year of
Foundation
2004
Volumes
per Year
1
Issues
per Year
1
Founder Magyar Tudományos Akadémia  
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1786-335X (Print)
ISSN 1787-0321 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Sep 2023 0 0 0
Oct 2023 0 0 0
Nov 2023 0 65 34
Dec 2023 0 450 44
Jan 2024 0 505 19
Feb 2024 0 1104 18
Mar 2024 0 16 0