Biological Shift Factor Research Articles

Modeling mango ripening during shelf life based on pulp color nondestructively measured by time-resolved reflectance spectroscopy

Non-destructive techniques could help mango growers to pick fruit at the proper maturity degree and to monitor fruit quality during storage and marketing to satisfy the consumer expectations. The absorption coefficient non-destructively measured at 540 nm (μa540) by time-resolved reflectance spectroscopy (TRS) was shown to be a maturity index for mango fruit and was highly correlated with pulp color parameters and carotenoids content. The aim of this work was to model μa540 using the biological shift factor theory to verify if μa540 is able to assess the maturity degree of individual mango fruit and then to use μa540 to model pulp color in order to relate μa540 to an important index of mango ripening.Mango fruit (Mangifera indica L. cv Tommy Atkins) at commercial maturity for ship and air transport maturities, were measured by TRS, ranked according to decreasing μa540 (decreasing maturity), randomized into six batches per transport maturity and analyzed for μa540 and pulp color parameters after 0, 1, 2, 5, 6 and 7 days of shelf life at 20 °C.The μa540 as a function of biological shift factor increased during ripening following a logistic/exponential model (R2adj=99%) with a faster rate in less mature fruit than in more mature ones. The changes in pulp color during mango ripening depended on fruit maturity (i.e., μa540) and on time of shelf life at 20 °C. By converting the μa540 into the biological shift factor it was possible to model the increasing trend of a*, b*, C*, and IY and the decreasing trend of L* and h° during the shelf life period explaining 91.2–99.8% of the variation and to differentiate mango fruit according to their biological age. Similarly to μa540, color changes occurred earlier in more mature fruit and later in less mature ones with the same pattern in time.There is a synchronization between changes of μa540 and changes of a*, b*, C* and yellowness during ripening in mango fruit which allows to use μa540 to sort fruit according to their maturity degree and then to optimize fruit management along the supply chain.

Size development of tomatoes growing in trusses: linking time of fruit set to diameter.

BACKGROUNDSize of fruit is an important issue in determining yield at harvest. Even under controlled conditions, variation between fruit and trusses can be considerable. As an easy to measure indication of size, the diameter of tomatoes growing in trusses was assessed in three experiments with different number of tomatoes per truss, as well as cultivars, and also by varying the level of ions in the recirculated drain water.RESULTSBy applying the von Bertalanffy growth model, more than 99% of the variation present could be explained by the time of fruit set for all tomatoes growing anywhere in the trusses. A linear relationship between time of fruit set and the biological shift factor, an indication of developmental age, was observed. Integrating this linear relationship in the analysis of the diameter data removed one stochastic variable (biological shift factor), effectively halving the number of parameters to be estimated.CONCLUSIONThe results of the present study indicate that the major part of the variation present in the diameter of tomatoes growing in trusses is the result of variation in the time of fruit set of individual fruits. The position within the greenhouse (i.e. local differences in assimilates supply) exerted only a minor effect on diameter development. Accordingly, the time of fruit set largely determines fruit size. Likely, growing conditions before fruit set are crucial for final fruit size. The time of fruit set of each tomato in the truss and the local growing conditions within the greenhouse that affect assimilate supply need to be assessed accurately for a reliable size prediction.

Modelling quality and maturity of ‘Namdokmai Sithong’ mango and their variation during storage

‘Namdokmai Sithong’ mangoes, grown in carbon paper bags, are harvested at commercial maturity. As chlorophyll is virtually absent, they have a yellow skin colour changing only slightly during fruit ripening. This special characteristic, i.e., the absence of chlorophyll, conceals the variation within the batch in maturity stage and as well as in quality. Quality variables were assessed during storage by destructive (traditional penetration test Fpff, flesh colour L*a*b*, total soluble solids TSS and titratable acidity TA) or non-destructive techniques (limited distance compression Fcomp, skin colour L*a*b*). Three batches of mango in export quality grown at different cultivation locations were stored at four constant temperatures (kinetic experiment) and at 13 °C for 14 d then at 25–28 °C (dynamic experiment). Huge variation was observed in all quality variables in both systems mainly due to differences in maturity of individual fruit. Kinetic models are presented to describe behaviour of quality variables including the biological variation expressed as biological shift factor. A logistic model (logis) was used for all variables, except flesh colour b* value and TSS in the dynamic experiment, where a first order production model (FOP) was applied to analyse the data. Destructively measured data were first grouped based on rank at every measuring time (probelation). Rank number and fruit number were used as an index in non-linear regression. High explained parts (R2adj above 90%) were obtained for firmness and skin colour (a* and b*) for all temperatures of the kinetic experiment separately as well as combined. The (lower) asymptote values for limited compression firmness (Fcomp) were somewhat different at different temperatures, which is again an indication of large difference in maturity. In absence of chlorophyll, a strong relation was found between the biological shift factors of Fcomp and all colour values. During first storage period at 13 °C of the dynamic experiment, most obtained explained part (R2adj) but not all were well over 90%. When both temperature schemes (13 °C and ambient temperature) were analysed together, the rate constant after the temperature switch for firmness and colour was higher indicating a faster fruit ripening. The two cultivation locations generated a substantial difference in colouration, but not so much in terms of firmness. A strong correlation between Fcomp, TSS and TA was revealed. All these results indicate that observed variation in any of the quality variables are all linked directly to variation in maturity. In fruit industry, the biological variation is ignored completely which results in a heterogeneity in the final product at the consumers. Modelling techniques have studied can take care of this variation and could help to improve quality management in the production line in order to assure a constant level of the fruit quality.

From fruitlet to harvest: Modelling and predicting size and its distributions for tomato, apple and pepper fruit

Size is an important property of all fruit as it determines, together with crop load, fruit yield. The aim of this study is to model the increase in fruit size during cell expansion based on diameter or length and to predict fruit size and volume, including the variation in size and volume, at harvest. The size of individual fruit during growth of fruit (four apple, two tomato and six pepper cultivars) was measured from early fruit set until harvest at several seasons. Size data were evaluated as diameter and length using the von Bertalanffy growth model, adapted to include the variation in size between individuals and the effect of growing temperature. The adapted von Bertalanffy model describes the increase in diameter and length and their variation. For any fruit type, size increased along the same generic growth model. A single stochastic variable, the biological shift factor, was sufficient to describe the variation in development time, initial size and maximum size. For all batches the percentage variation explained for were well over 93% with growth rate constants similar per species and only a minor effect of growing temperature.Prediction of the maximum size distribution based on measurements half way the cell expansion period is explored and discussed. Size and its distribution can be predicted based on a single measuring point in time but with two or three points in time the prediction system becomes more robust.

DYNAMICS OF FIRMNESS AND COLOUR OF THAI MANGO CULTIVAR 'NAM DOK MAI SI-THONG'

Firmness and colour of Thai mango (‘Nam Dok Mai Si-Thong’) were assessed (firmness at two opposite sides) at regular intervals during storage at 13°C for 14 days, followed by storage at 28°C for another 8 days. The mangoes were individually labelled and the obtained data were analysed using a logistic decay for firmness and all colour attributes (L*, a*, and b*), including the biological variation within in a batch. The variation in maturity was expressed as biological shift factor. Explained parts of around 90% were obtained for all variables. The biological shift factors are normally distributed. No major difference was found for firmness between both sides of the mangoes. Hardly any relation was found between the biological shift factors of the colour attributes with that of firmness. That confirms that (skin) colour cannot be used to assess the maturity status of firmness in this cultivar. As expected, a pronounced effect of temperature was found, but only on the rate constant of the softening and colour decay process. At 28°C, softening was almost 1.65 times faster and colour development 5 times faster for a* and b* colour aspects than at 13°C. The other model parameters and the model structure were, however, not affected by temperature. That means that in the supply chain, the same model can be used to assess and predict the behaviour of colour and firmness in the entire chain.

A CASE STUDY OF A DECISION SUPPORT SYSTEM ON MANGO FRUIT MATURITY

Mango fruit maturity can be difficult to determine from external attributes. Assessment of parameters of fruit on tree (dry matter, internal flesh colour) relevant to estimation of fruit maturity was undertaken with a handheld (near infrared spectroscopic) system. Measurement error on dry matter was low (typical RMSEP 0.6% DM). Repeated measurements on the same individual fruit from 78 different blocks across two farms demonstrated that each piece of fruit was on a similar, but individual, maturation trajectory, with a time offset. The offset was presumably related to date of pollination or environmental conditions around the fruit (e.g., inner or outer canopy). A non-linear indexed regression model, coupled with the use of a ‘biological shift factor’, was used to describe the time series data. Estimated biological shift factors were larger for dry matter than flesh colour, indicative of an earlier change in dry matter, albeit at a lower rate. Differences between blocks within a farm and between two farms were small, indicating the maturation processes were independent of local conditions. This technique could be used to trace the source of variation within a block (e.g., to location in canopy or plant water status), towards the goal of reducing this variation, leading to crops of greater uniformity.

NON-DESTRUCTIVE HARVEST MEASUREMENT CAN PREDICT RATE OF SOFTENING OF FAST- AND SLOW-MELTING PEACHES

Peaches are harvested selectively based on size and color, both peel background color and extent of blush. The background color of the fruit changes from green to yellow as chlorophyll disappears, and in many cultivars with red coloration this is hard to determine. A portable instrument that measures I AD (the index of absorbance difference between 670 and 720 nm) was used for monitoring the progression of ripening in two peach cultivars. I AD was measured at harvest in 'Oded' (an early season, fast melting flesh peach) and 'September Snow' (a late season, slow melting flesh peach). After harvest the fruits were divided into comparable lots and held at 20°C. The I AD and firmness were measured daily on a different lot. Two analytical methods were applied to the data. In the first, firmness decrease during shelf life was analyzed by nonlinear regression, using the I AD measured at harvest to determine the maturity of the fruit, and to estimate the biological shift factor (BSF) which is related to the biological age of fruit. In the second, a prediction equation for firmness was developed as a function of initial I AD , giving a table of the probability of a fruit to be less than 20 N on each day of shelf life. In both these methods the measurement of I AD at harvest could be used to separate fruit into different groups according to their rate of softening, and, therefore, potential shelf life.

Optical properties, ethylene production and softening in mango fruit

Firmness decay, chlorophyll breakdown and carotenoid accumulation, controlled by ethylene, are major ripening events in mango fruit. Pigment content and tissue structure affect the optical properties of the mesocarp, which can be measured nondestructively in the intact fruit by time-resolved reflectance spectroscopy (TRS). This work is aimed at improving the maturity assessment in mango (Mangifera indica L. cv Haden) from Brazil, using TRS absorption in both the carotenoid and chlorophyll regions in order to develop a model for fruit ripening. Scattering and absorption in the 540–900nm spectral range by TRS, ethylene production and respiration rate, and firmness, were measured in one day on each individual fruit of a sample covering the range of maturity. The fruit displayed a variability which was attributed to the different biological age. Absorption spectra showed two peaks at 540 and 670nm, corresponding respectively to the tail of carotenoid absorption and to chlorophyll-a absorption. Carotenoids increased substantially only in fruit where chlorophyll had almost disappeared. The absorptions at 540 and 670nm, which described the maturity state of each fruit relative to the range of each wavelength, were combined in one index of biological age (biological shift factor) for each fruit and used in logistic models of ethylene increase and firmness decay respectively. The model explained about 80% of the variability in ethylene production rate. A similar result was obtained for firmness when scattering was added in the model. The combination of absorption at 540 and 670nm measured by TRS in the intact fruit can be used to classify mango fruit according to maturity and to predict the ripening of individual fruit.

Maturity assessment at harvest and prediction of softening in an early and late season melting peach

For peaches and nectarines, establishing fruit maturity at harvest is a crucial issue, since fruit shelf-life potential and quality are closely related to fruit maturity. A portable instrument that measures IAD (the index of absorbance difference between 670 and 720nm) was used for monitoring the progression of ripening in two peach cultivars. IAD was measured at harvest in ‘Oded’ (an early season, fast melting flesh peach) and ‘September Snow’ (a late season, slow melting flesh peach) fruit. After harvest the fruit were divided into comparable lots and held at 20°C. The IAD and firmness were measured daily on a different lot. Firmness decay during shelf-life was analyzed by non-linear regression, using the IAD measured at harvest to determine the maturity of the fruit and to estimate the biological shift factor (BSF) which is related to the biological age of fruit. A logistic model developed for firmness decay with maturity assessment by time-resolved reflectance spectroscopy was used, where the time-resolved measurement was substituted by IAD. The R2 for ‘Oded’ softening was 0.86, while that of ‘September Snow’ was 0.66. IAD decay was analyzed by non linear mixed effects analysis, obtaining R2adj higher than 0.96 for both cultivars. IAD showed the same decay rate in the two cultivars, while softening rate was five times lower in the late ‘September Snow’ than in the early ‘Oded’ fruit. The measurement of IAD at harvest could be used to separate fruit into different groups according to their potential shelf-life.

Effect of location in the canopy on the colour development of three apple cultivars during growth

Homogeneity in appearance is one of the quality aspects asked for in the supply chain. Decreasing the biological variation in batches of harvested apples (cultivars Braeburn, Fuji and Gala) becomes increasingly important. Skin colour is one of the aspects that determine both optimal harvest and stage of development. Skin colour is affected by location in the canopy. The rules of development of biological variation are now established and will be used on skin colour data. The Minolta colour aspects a*, b* and L* measured before commercial harvest change in a sigmoidal fashion and can be analysed including the biological variation, with a logistic model in indexed nonlinear regression, obtaining explained parts of above 90%. The mechanism of colour change is not affected by state of development or location in the canopy. The location in the canopy affects the intensity of both red and green colouring compounds. The variation in colouration is not affected by the location in the canopy. The red-coloured apple cultivar (Gala) depends more on the location in the canopy than the less-coloured cultivars (Fuji and Braeburn). The colour development in Fuji apples is considerably slower, with a much larger variation in stage of development. The location in the canopy affects all aspects of biological variation (biological shift factor and asymptotic starting level of colouration) for all three colour aspects L*, a* and b*, but only the mean value, not the standard deviation. The biological shift factors per colour aspects are linearly related. Once induced, variation remains constant during development.

Maturity assessment at harvest and prediction of softening in a late maturing nectarine cultivar after cold storage

The absorption coefficient μ a measured at 670 nm in fruit pulp at harvest by time-resolved reflectance spectroscopy (TRS) has been shown to be a good maturity index for early nectarine cultivars. By including individual fruit maturity as a biological shift factor (BSF) into a kinetic model for softening it is possible to select fruit with different shelf-life potential. The BSF approach combined with TRS measurement and kinetic modeling of firmness was applied to a late maturing nectarine cultivar (‘Morsiani 90’), ripened at 20 °C after harvest or after storage at 0 °C and 4 °C, the latter conditions inducing chilling injury. At harvest the absorption coefficient μ a had low values and low variability, indicating advanced maturity, while firmness was similar to that of early cultivars. The softening model took into account these differences, showing parameters similar to those of the early cultivars with the exception of the softening rate which was 2–6 times lower, indicating a slower softening in ‘Morsiani 90’ fruit. Decay of μ a at 20 °C was also slower. Softening continued during storage at 4 °C, but not at 0 °C. After storage at 0 °C softening was resumed similarly to non-stored fruit, but with much variability. Fruit stored at 4 °C, which showed chilling injury, had a softening rate at 20 °C significantly higher than that of 0 °C fruit. It is suggested that the same changes in cell wall metabolism which induce the appearance of chilling injury also affect firmness and increase softening rate.

ASSESSING THE HARVEST MATURITY OF BRAZILIAN MANGOES

ISHS International Symposium Postharvest Pacifica 2009 - Pathways to Quality: V International Symposium on Managing Quality in Chains + Australasian Postharvest Horticultural Conference ASSESSING THE HARVEST MATURITY OF BRAZILIAN MANGOES

Prediction ability of firmness decay models of nectarines based on the biological shift factor measured by time-resolved reflectance spectroscopy

The maturity of nectarines at harvest can be assessed by measuring the absorption coefficient at 670 nm ( μ a) with the non-destructive technique of time-resolved reflectance spectroscopy (TRS). A kinetic model links μ a, converted into the biological shift factor (BSF), to firmness decrease during ripening; in this way the firmness decay model includes the variations in maturity at harvest, thereby allowing prediction of shelf-life for individual fruit. In order to study how this methodology could be practically used at the time of harvest, when μ a can be measured non-destructively on all fruit, while the destructive measurement of firmness can only be done on a small sample, various firmness decay models were developed using either data at harvest or within 1–2 d after harvest from previous experimental research with nectarines carried out over a 5-year period. These models were then tested for prediction and classification ability by comparing the predicted firmness and class of usability to the actual ones measured during ripening and their performance compared to that of models based on data during the whole shelf-life. Our results suggest that the methodology might be used as a management tool in the nectarine supply chain. Independently from the actual softening rate, the classification at harvest based on μ a is able to segregate fruit of different quality and maturity according to their softening behaviour during shelf-life. Among the various models, those estimated using data at harvest and after 24 h of shelf-life had better performance than those based only on data at harvest. In the 2002 and 2005 seasons, this model showed a classification ability very close to that of models based on data during the whole shelf-life. However, its performance in the 2004 season was not so good, because it could not take into account the influence of cold storage periods prior to shelf-life. All the steps necessary to apply this methodology are detailed.

Biological variation in the colour development of Golden Delicious apples in the orchard

AbstractBACKGROUND: In managing apple orchards, crop load and rate of nitrogen (N) fertilisation are two factors with a significant influence on fruit quantity and quality, because they affect all physiological processes in the tree. Both factors are strongly related to external and internal fruit quality, especially to skin colour, sugar and acid contents and mineral composition, and consequently to the keeping quality of fruits. The aim of this study was to assess the effects of both factors (three crop load levels and two N fertilisation levels) on the colour development of Golden Delicious apples during the last month on the tree in two consecutive seasons. Data on skin colour (L*, a*, b* values) were analysed using nonlinear mixed effects modelling to extract information on the variation in biological shift factor for colour and to link this variation to the different strategies used concerning N fertilisation and crop load.RESULTS: The major source of information is contained in the a* value. The behaviour of the a* value could be described by a logistic or an exponential model depending on the season and the experimental set‐up. Nonlinear mixed effects analysis estimating the biological shift factor (maturity) for each individual fruit (random effect) while estimating the rate constant of the decolouration process in common (fixed effect) resulted in explained parts well over 95%.CONCLUSION: The variation in maturity stage between individual fruits is large. Season has the most profound effect on the estimated values, far more important than that of crop load or fertilisation level. The magnitude of variation in colour due to crop load and N fertilisation is not too large. Its effect on the maturity stage of fruits is more profound: the higher the crop load, the higher the variation. The effect of fertilisation seems to be opposite: the higher the fertilisation level, the lower the variation. Copyright © 2009 Society of Chemical Industry

Time-resolved Reflectance Spectroscopy as a management tool in the fruit supply chain: an export trial with nectarines

At harvest, fruit shows variation in maturity stage. With Time-resolved Reflectance Spectroscopy (TRS), the maturity of nectarines at harvest can be assessed by measuring the absorption coefficient at 670 nm (μa), near the chlorophyll peak, in the fruit flesh. A kinetic model has been developed linking the absorption coefficient, expressed as the biological shift factor (Δt*), to firmness decrease during ripening. As the decrease in μa in nectarines is linked with softening, shelf life for individual fruit can be predicted. In order to verify the applicability of this methodology in the supply chain, about 1000 nectarines were measured at harvest by TRS, graded into six classes of usability based on the prediction of their individual softening (‘will never ripe’, ‘dangerously hard’, ‘transportable’, ‘ready to eat-firm’, ‘ready to eat-ripe’, ‘overripe’) and transported from Italy to the Netherlands by a regular temperature-controlled truck. On arrival, fruit was kept at 20 °C and tested for sensory softness (finger feeling) after 5 and 13 days of shelf life. The classes ‘will never ripe’, ‘dangerously hard’ and ‘overripe’ were correctly predicted, as the first two did not soften and the last one was too soft and subject to rot. The intermediate classes showed sufficient firmness to be transported and sufficient ripening potential to satisfy consumers.

Biological variance in the colour of Granny Smith apples: Modelling the effect of senescence and chilling injury

The colour of ‘Granny Smith’ apples, harvested from three orchards at two stages of maturity, was measured individually using the CIE L* a* b* system during storage in regular atmosphere at three temperatures: 1, 4 and 10 °C. A model was developed based on a simplified mechanism to describe the development of the apple colour during storage as affected by senescence (aging) and chilling injury. Monitoring of individual apples made it possible to include and to describe the biological variance of colour in batches of apples and to correct the colour of each apple individually for its own biological shift factor (biological age; random effect). The biological shift factor is related to the initial condition and range of colour change. The rate of the colour development was estimated in common (fixed effects) for all batches using non-linear mixed effects regression analysis. The variance accounted for by the developed model, including effects of temperature, harvest maturity and orchard location, was more than 95% on 3211 observations. The overall reaction rate constant of decolouration, combining the effects of senescence and chilling injury, was found to depend on temperature. The temperature that showed the lowest overall reaction rate of decolouration is 8 °C, which is in contrast with the currently recommended storage temperature for ‘Granny Smith’ apples.

HOW TO CHOOSE CRITERIA TO HARVEST APPLES. THE DYNAMICS OF MATURITY

The storage behaviour of apples is simulated with a model based on the changes in green ground colour of three apple cultivars (`Elstar?, `Topaz? and `Pinova?) during ripening on the tree in the same orchard in three consecutive seasons, and also the effects of at-harvest maturity criteria. Taking the variation over the seasons into account (mainly found in the potential greenness), using a fixed fraction of ground colour at the moment of harvest as a harvest criterion was found to exhibit a better performance than using a harvest criterion of a fixed colour. The second part of the paper deals with theoretical relations between the product properties at the moment of harvest. These relations constitute the calibration curve per season for harvest indices like the Streif index. Variations in properties due to different seasonal conditions can affect the range of colour, as indicated in previous examples, and the synchronisation in the biological shift factor for different properties (here colour and firmness). The first effect (colour range) generates a change in slope between the two variables (colour and firmness), the second one a parallel shift. These findings can be used to improve the general performance of harvest indices over the seasons and orchards.

Assessing harvest maturity in nectarines

The maturity at harvest of nectarines can be assessed with the novel technique of time-resolved reflectance spectroscopy (TRS) measuring the light absorption at 670 nm in the fruit flesh. A kinetic model was developed that links this absorption coefficient ( μ a), expressed as the biological shift factor, to firmness decrease during ripening. The model thus includes the variations in maturity at harvest of the individual fruit. In non-linear regression analyses of data from four different seasons, the explained part ranged from 81 to 92% for cv ‘Spring Bright’. The kinetic parameters values obtained were highly similar in different seasons. The results suggest that in nectarines the degreening of fruit flesh, expressed as TRS absorption at 670 nm, is synchronised with the softening. The distribution of μ a was skewed, but that of the biological shift factor, derived from μ a, was normal. By measuring the colour of fruit flesh with the non-destructive and very rapid TRS technique, and applying the model parameters, individual fruit can be graded at harvest into classes of usability, selecting fruit with different ripening stages for different market segments and predicting their softening time, thereby guaranteeing sufficient firmness to transport the fruit and sufficient ripening potential to reach good eating quality upon arrival in the receiving regions or countries. Consumers therefore will be more satisfied, encouraging them to repeated purchase. Retailers and wholesalers will be more satisfied since fruit unfit for sale not longer needs to be transported.

Biological Variation in Ripening of Nectarines

Biological Variation in Ripening of Nectarines The optimal harvest date of nectarines can no longer be based on the colour since newer cultivars have an intensive blush, even in the unripe stage. Measuring the absorption of the fruit flesh by Time Resolved Spectroscopy (TRS) at 670 nm, provide information on the ripening stage and the variation in ripening stages of batches of fruit. Since each individual fruit is harvested and measured at some arbitrary stage of development, the analysis of the data gathered was based on the system of the biological shift factor (Tijskens et al. 2005) applying non-linear mixed effects regression analysis. The results show that μa, and therefore also the fruit flesh colour and chlorophyll content, change according a sigmoidal pattern, which was approximated with a symmetrical logistic function. The explained parts obtained (R2 adj) were about 0.97 for storage at 20°C and about 0.86 at 10°C. The rather large variation on apparent behaviour between individual fruit, each with its own stage of development, was nicely taken care of by the system of the biological shift factor. During ripening, the distribution of the biological shift factor was constant and normal, while the distribution of μa changed. Also that change in distribution over time is nicely described by the system and models used.

Effects of maturity on chlorophyll-related absorption in nectarines, measured by non-destructive time-resolved reflectance spectroscopy

The ripening of fruits like nectarines and peaches is characterised by the decrease in chlorophyll content or colour of the fruit flesh. However, measuring the colour of fruit flesh is usually not conducted, as there is a lack of suitable techniques. Time-Resolved Reflectance Spectroscopy (TRS) is a novel technique that can measure the light absorbed by the fruit flesh. Using a laser light beam at 670 nm, the chlorophyll content of the fruit flesh is assessed without disrupting the natural structure of the fruit. Data on individual fruits of different maturity at harvest were analysed based on a deterministic model using mixed effect non-linear regression. The results indicate that an analysis of data combined from pre- and post-harvest is possible. Using the theory of Biological Shift Factor, an explained part of 97% is obtained at 20° C and 85% at 10° C.