High End Quality Measuring in Mango Drying through Multi-Spectral Imaging Systems

Abstract

In modern fruit processing technology, non-destructive quality measuring techniques are sought for determining and controlling changes in the optical, structural, and chemical properties of the products. In this context, changes inside the product can be measured during processing. Especially for industrial use, fast, precise, but robust methods are particularly important to obtain high-quality products. In this work, a newly developed multi-spectral imaging system was implemented and adapted for drying processes. Further it was investigated if the system could be used to link changes in the surface spectral reflectance during mango drying with changes in moisture content and contents of chemical components. This was achieved by recovering the spectral reflectance from multi-spectral image data and comparing the spectral changes with changes of the total soluble solids (TSS), pH-value and the relative moisture content xwb of the products. In a first step, the camera was modified to be used in drying, then the changes in the spectra and quality criteria during mango drying were measured. For this, mango slices were dried at air temperatures of 40–80 °C and relative air humidities of 5%–30%. Samples were analyzed and pictures were taken with the multi-spectral imaging system. The quality criteria were then predicted from spectral data. It could be shown that the newly developed multi-spectral imaging system can be used for quality control in fruit drying. There are strong indications as well, that it can be employed for the prediction of chemical quality criteria of mangoes during drying. This way, quality changes can be monitored inline during the process using only one single measuring device.

Rapid and non‐destructive determination of total phenolic contents using UV–NIR spectroscopy of dehydrated mushroom (Lentinus edodes)

“…Likewise, MSI system performed successfully in adulterant identification of meat products, compromising the ethical and religious concerns of different cultures (Alamprese et al, 2013). The novelty, importance, and precise prediction of this rapid and non‐destructive technique have also been revealed during water level measurement of dehydrated hot‐air dried shiitake mushroom (Younas, Mao, Liu, Liu, et al, 2021), product quality of hot‐air dried red beet (de França et al, 2023), prediction of moisture content of melon (Netto et al, 2021), moisture and shrinkage ration of carrot slices (Yu et al, 2020), mango quality enduring dehydration (Jödicke et al, 2020), determination of phenolic content in Buddha tea (Xiong et al, 2015), and examination of in‐shell infested almonds (Yu et al, 2019).…”

Catalase Activity in Hot-Air Dried Mango as an Indicator of Heat Exposure for Rapid Detection of Heat Stress

“…Among the dried fruits, mango is very popular and is usually dried at temperatures ranging from 40 • C to 80 • C, to a target moisture content of 10-15 g 100 g −1 wet basis [13,14]. Several researchers have extensively documented the effects of drying temperatures on quality attributes such as texture, color, total soluble solids, sugars, fiber, moisture content, vitamins, minerals, antioxidants, volatile compounds, carotenoids, phenolic content and phytochemicals of dried mangoes [3,4,11,12,[15][16][17][18][19][20].…”

Preliminary Study for Inspecting Moisture Content, Dry Matter Content, and Firmness Parameters of Two Date Cultivars Using an NIR Hyperspectral Imaging System

“…Here, the running and measuring processes of the HSI technique are carried out by monitoring and storing the data in different modes of reflectance, transmittance, and interactance for analyzing food quality [Gowen et al (2007); Lorente et al (2012); ElMasry et al (2012); Qin et al (2013)]. These measurements are: the quantities of moisture content, TSS, soluble solid content (SSC), and firmness in the case of fresh okra fruits [Xuan et al (2021)], nectarines [Huang et al (2021)], tomatoes [Liu et al (2015)], strawberries [ElMasry et al (2007)], apples [Crichton et al (2018); Noh et al (2007)], blueberries [Leiva-Valenzuela et al (2013)], plums [Li et al (2018)], sweet cherries [Pullanagari and Li (2021)], bananas [Rajkumar et al (2012)], pears [Li et al (2016)], nectarines [Munera et al (2018); Munera et al (2017)], mangoes [Jödicke et al (2020); Pu and Sun (2015)], and peaches [Lu and Peng (2006); Zhu et al (2016)].…”