Changes in Chlorophyll Fluorescence of Apple Fruit during Maturation, Ripening, and Senescence

Abstract

<jats:p>Trends in chlorophyll fluorescence for `Starking Delicious', `Golden Delicious' and `Law Rome' apple (<jats:italic>Malus</jats:italic> ×<jats:italic>domestica</jats:italic> Borkh.) fruit were examined during the harvest season, during refrigerated-air (RA) storage at 0 °C, following RA and controlled-atmosphere (CA) storage, and during a poststorage holding period at 22 °C. Fluorescence parameters of minimal fluorescence (Fo), maximal fluorescence (Fm), and quantum yield [(Fm-Fo)/Fm, otherwise denoted as Fv/Fm] were measured. During `Starking Delicious' fruit maturation and ripening, Fv/Fm declined with time, with the rate of decline increasing after the ethylene climacteric. During RA storage, all fluorescence parameters remained constant for approximately 2 weeks, then steadily declined with time for `Starking Delicious' fruit. Superficial scald was detected after Fv/Fm had declined from an initial value of 0.78 to ≈0.7. Fv/Fm was consistently higher for CA-stored fruits than for RA-stored fruits. We were able to resegregate combined populations of “high-quality” (CA) and “low-quality” (RA) `Law Rome' fruit with 75% accuracy using a threshold Fv/Fm value of 0.685, with only 5% RA-stored fruit incorrectly identified as being of high quality. During a poststorage holding period, Fo, Fm, and Fv/Fm correlated well with firmness for `Starking Delicious', but not for `Golden Delicious' fruit, which were already soft. Fo and Fm were linearly correlated with hue angle for 'Golden Delicious' fruit, decreasing as yellowness increased. The accuracy, speed of assessment, and light-based nature of fluorescence suggests that it may have some practical use as a criterion to assist in sorting apple or other chlorophyll-containing fruit or vegetables on commercial packing lines.</jats:p>

Some physical and chemical properties of ambarella (Spondias cytherea Sonn.) at three different stages of maturity

“…The b à values also increased with the gradual change from green to yellow in the skin colour. These changes are caused by the degradation of chlorophyll and the accumulation of carotenoid pigments (Song et al, 1997). L (lightness) was highest in ripe fruits (which appeared more yellowish), while CðC à ¼ p ða Ã Þ 2 þ ðb Ã Þ 2 ), the colour saturation, increased in fruit flesh with maturation.…”

Fluorescence Spectroscopy Measurement for Quality Assessment of Food Systems—a Review

“…Chlorophyll fluorescence has been used as an intrinsic probe to determine the physiological status of whole plants and plant organs (Song et al 1997). This component has been considered an efficient probe for monitoring apples during maturation, ripening, and senescence (Song et al 1997).…”

“…This component has been considered an efficient probe for monitoring apples during maturation, ripening, and senescence (Song et al 1997). Recently, fluorescence spectroscopy has been considered to have the potential for assessing the mealiness of apples (Moshou et al 2005), since relatively good correlation was obtained between mealiness and fluorescence spectra.…”

Automatic sorting of Chinese jujube (Zizyphus jujuba Mill. cv. ‘hongxing’) using chlorophyll fluorescence and support vector machine

“…Present sorting systems also tend to include spectral reflectance (Upchurch et al, 1990;Guyer et al, 1996) and hyperspectral imaging (Lu, 2003;Mehl et al, 2004) which have been widely used for assessing the quality aspects of agricultural produce. In addition, a new rapid and promising nondestructive fluorescence spectroscopic method has been used as an indirect quality evaluation of several chlorophyll-containing fruits (Song et al, 1997;DeEll et al, 1996;Bron et al, 2004;Kolb et al, 2006;Hyun and Renfu, 2007;Kuckenberg et al, 2008), because of ChlF yield decrease with decreased photosynthetic activity and chlorophyll content during fruit ripening and senescence (Smillie et al, 1987;Sanxter et al, 1992). Support vector machine (SVM) is a new algorithm developed by the machine learning community (Vapnik, 1989(Vapnik, , 1995.…”