Non-invasive assessment of fruit: Attributes other than sweetness

Fresh fruit eating quality, as defined by taste, texture (mouth feel) and smell, can be indexed by a number of attributes, including total soluble solids (TSS), total titratable acidity (TTA), firmness and aroma. Eating quality is further defined by a range of internal defects (e.g. dryness defect in mandarin). Available technologies for non-invasive assessment of these attributes are reviewed. The two technologies which have reached a stage of commercial adoption by the fruit industry are short wave near infrared spectroscopy (SWNIRS) and firmness assessment using impact or acoustic based techniques. The SWNIRS technique apparently has utility in the assessment of fruit TSS and dry matter (DM), but literature reports on use for other attributes (e.g. individual soluble sugar levels or firmness) are less convincing (e.g. failing to demonstrate prediction of independent validation sets). Based on the use of the Zeiss MMS1 NIR enhanced spectrometer module, SWNIRS (700 - 1100 nm) was demonstrated to be capable of analysis of citric acid (CA) in aqueous solution with a root mean square error of prediction (RMSEP) of 0.34 % w/v. Difference spectra of pure aqueous solution of CA (i.e. subtraction of the water spectrum) supported interpretation of the CA spectra and partial least squares regression (PLSR) model regression coefficients, with absorption at 970 nm attributed to an O-H stretching band. For starch in aqueous solution, excellent model results (typical root mean square error of crossvalidation (RMSECV) = 0.30 % w/v) were interpreted in terms of scattering caused by the starch grains. The influence of temperature and salt (NaCl) on SWNIR spectra of model solutions was also characterised. Short wave near infrared spectroscopy was used in the development of models for a range of internal quality attributes (TTA, TSS in fruit varying in starch content, firmness, internal flesh colour, maturity level, and flesh ‘dryness defect’) of intact fruit. In each case the wavelength range and the number of factors used in the PLS model was optimised. In general an interactance mode was adopted, but in some cases presentation geometry (angle between light source, sample and detector) was also optimised. The SWNIRS technique was demonstrated to be effective in the assessment of DM content of a number of commodities (typical RMSEP around 1% DM). Sorting on DM spectra was shown to allow for removal of immature mangoes (fruit that will be slow or fail to ripen). Further, it was demonstrated that spectra collected of hard green mango could be directly related to TSS of fully ripe fruit. The SWNIRS technique was also demonstrated to be effective in the assessment of mango fruit internal colour (as flesh Hunter L a b). However, the SWNIRS technique was not recommended for assessment of TSS of intact fruit of varying starch level (i.e. in ripening mango or banana). In banana, for example, the PLSR model on TSS was interpreted in terms of assessment of peel chlorophyll content, representing an indirect assessment of TSS. With an RMSECV >0.1 % and a RMSEP = 0.3 % w/v, SWNIRS models were of marginal value in prediction of TTA of high acid fruit (e.g. lime, x ± standard deviation (SD): 7.3 ± 0.51 %), and of no value in prediction of low TTA fruit (e.g. peach, x ± SD: 0.88 ± 0.17 %). The SWNIR calibration models on fruit firmness achieved a Rcv2 >0.8, but in prediction of independent sets Rp 2 was <0.7. An acoustic technique based on sound velocity (SV) was better suited to assessment of fruit firmness. The SV decreased during ripening in mango, banana, peach and tomato fruit. The rate of this change was different to that of a penetrometer assessment, indicating that the two methods are assessing different mechanical properties of the fruit. A dryness defect of cultivar (cv.) Imperial mandarin was associated with cell proliferation within the juice sacs, and this character was associated with the observed colour of the juice sacs (high luminosity value). It was hypothesised that this character would decrease light transmission through affected fruit. However, fruit juice sac luminosity also varied with fruit maturity, and SWNIRS model performance was not consistent. Practical implementation of the SWNIRS technique for on-line sorting of defect fruit would involve constant model updating. In conclusion, the factors contributing to a successful implementation of the SWNIRS technique to a given application are summarised, and future directions in instrumentation and chemometrics discussed

Non-invasive assessment of fruit: Attributes other than sweetness

Fresh fruit eating quality, as defined by taste, texture (mouth feel) and smell, can be indexed by a number of attributes, including total soluble solids (TSS), total titratable acidity (TTA), firmness and aroma. Eating quality is further defined by a range of internal defects (e.g. dryness defect in mandarin). Available technologies for non-invasive assessment of these attributes are reviewed. The two technologies which have reached a stage of commercial adoption by the fruit industry are short wave near infrared spectroscopy (SWNIRS) and firmness assessment using impact or acoustic based techniques. The SWNIRS technique apparently has utility in the assessment of fruit TSS and dry matter (DM), but literature reports on use for other attributes (e.g. individual soluble sugar levels or firmness) are less convincing (e.g. failing to demonstrate prediction of independent validation sets). Based on the use of the Zeiss MMS1 NIR enhanced spectrometer module, SWNIRS (700 - 1100 nm) was demonstrated to be capable of analysis of citric acid (CA) in aqueous solution with a root mean square error of prediction (RMSEP) of 0.34 % w/v. Difference spectra of pure aqueous solution of CA (i.e. subtraction of the water spectrum) supported interpretation of the CA spectra and partial least squares regression (PLSR) model regression coefficients, with absorption at 970 nm attributed to an O-H stretching band. For starch in aqueous solution, excellent model results (typical root mean square error of crossvalidation (RMSECV) = 0.30 % w/v) were interpreted in terms of scattering caused by the starch grains. The influence of temperature and salt (NaCl) on SWNIR spectra of model solutions was also characterised. Short wave near infrared spectroscopy was used in the development of models for a range of internal quality attributes (TTA, TSS in fruit varying in starch content, firmness, internal flesh colour, maturity level, and flesh ‘dryness defect’) of intact fruit. In each case the wavelength range and the number of factors used in the PLS model was optimised. In general an interactance mode was adopted, but in some cases presentation geometry (angle between light source, sample and detector) was also optimised. The SWNIRS technique was demonstrated to be effective in the assessment of DM content of a number of commodities (typical RMSEP around 1% DM). Sorting on DM spectra was shown to allow for removal of immature mangoes (fruit that will be slow or fail to ripen). Further, it was demonstrated that spectra collected of hard green mango could be directly related to TSS of fully ripe fruit. The SWNIRS technique was also demonstrated to be effective in the assessment of mango fruit internal colour (as flesh Hunter L a b). However, the SWNIRS technique was not recommended for assessment of TSS of intact fruit of varying starch level (i.e. in ripening mango or banana). In banana, for example, the PLSR model on TSS was interpreted in terms of assessment of peel chlorophyll content, representing an indirect assessment of TSS. With an RMSECV >0.1 % and a RMSEP = 0.3 % w/v, SWNIRS models were of marginal value in prediction of TTA of high acid fruit (e.g. lime, x ± standard deviation (SD): 7.3 ± 0.51 %), and of no value in prediction of low TTA fruit (e.g. peach, x ± SD: 0.88 ± 0.17 %). The SWNIR calibration models on fruit firmness achieved a Rcv2 >0.8, but in prediction of independent sets Rp 2 was <0.7. An acoustic technique based on sound velocity (SV) was better suited to assessment of fruit firmness. The SV decreased during ripening in mango, banana, peach and tomato fruit. The rate of this change was different to that of a penetrometer assessment, indicating that the two methods are assessing different mechanical properties of the fruit. A dryness defect of cultivar (cv.) Imperial mandarin was associated with cell proliferation within the juice sacs, and this character was associated with the observed colour of the juice sacs (high luminosity value). It was hypothesised that this character would decrease light transmission through affected fruit. However, fruit juice sac luminosity also varied with fruit maturity, and SWNIRS model performance was not consistent. Practical implementation of the SWNIRS technique for on-line sorting of defect fruit would involve constant model updating. In conclusion, the factors contributing to a successful implementation of the SWNIRS technique to a given application are summarised, and future directions in instrumentation and chemometrics discussed

Non-invasive techniques for measurement of fresh fruit firmness

A sound velocity technique and visible–short wave near infrared (400–1100 nm) interactance spectroscopy were considered in the context of the assessment of fruit firmness in intact banana, mango and peach fruit. The velocity of a vibration (‘sound’) wave moving through the fruit decreased during ripening of mango (from 84 to 39ms−1), banana (29 to 14) and peach (28 to 15) fruit. Fruit firmness assessed using a penetrometer (Fpen) was linearly correlated (R2 > 0.8) with sound velocity in mango, but not peach or banana. Spectra were related to the penetrometer and sound velocity readings using partial least squares regression. A cross-validation result of R2 = 0.92, 0.86 and 0.79 for the penetrometer reading and R2 = 0.88, 0.77 and 0.58 for the sound velocity reading was achieved for banana, mango and peach fruit,respectively. However, these results are likely to be a peel-related attribute for banana, potentially a skin pigment, as the optical geometry used would primarily optically sample the peel, rather than pulp tissue. Prediction results, involving independent data sets, were very poor (R2 < 0.75) for both the penetrometerand sound velocity readings in all three commodities, with the marginal exception of that for the penetrometer reading for banana (R2 = 0.76). The visible–short wave near infrared interactance spectroscopy technique is therefore not recommended for assessment of fruit firmness. The sound velocity technique is recommended for measurement of an index descriptive of the stage of ripening of mango, banana and peach fruit, although it does not assess the same character as a penetrometer reading.Crown Copyright © 2008 Published by Elsevier B.V. All rights reserved

Assessment of potato dry matter concentration using short-wave near-infrared spectroscopy

The utility of short-wavelength near-infrared spectroscopy (over the wavelength region 750–950 nm), used in a partial transmittance optical geometry, was assessed as a means of estimating the dry matter concentration of potato tubers. The sampling optics did not involve contact with the sample, and could be used on a moving stream of product. A prediction accuracy of R2 (correlation coefficient of determination) of 0.85 with a root mean square error of prediction (RMSEP) of 1.52% for intact, whole tubers and R2=0.95 and RMSEP=0.50% for sliced tubers was achieved. We conclude that short-wavelength near-infrared technology using a partial transmittance optical sampling geometry can be a useful tool for rapid assessment of tuber dry matter concentration prior to processing

Non-invasive techniques for measurement of fresh fruit firmness

A sound velocity technique and visible–short wave near infrared (400–1100 nm) interactance spectroscopy were considered in the context of the assessment of fruit firmness in intact banana, mango and peach fruit. The velocity of a vibration (‘sound’) wave moving through the fruit decreased during ripening of mango (from 84 to 39ms−1), banana (29 to 14) and peach (28 to 15) fruit. Fruit firmness assessed using a penetrometer (Fpen) was linearly correlated (R2 > 0.8) with sound velocity in mango, but not peach or banana. Spectra were related to the penetrometer and sound velocity readings using partial least squares regression. A cross-validation result of R2 = 0.92, 0.86 and 0.79 for the penetrometer reading and R2 = 0.88, 0.77 and 0.58 for the sound velocity reading was achieved for banana, mango and peach fruit,respectively. However, these results are likely to be a peel-related attribute for banana, potentially a skin pigment, as the optical geometry used would primarily optically sample the peel, rather than pulp tissue. Prediction results, involving independent data sets, were very poor (R2 < 0.75) for both the penetrometerand sound velocity readings in all three commodities, with the marginal exception of that for the penetrometer reading for banana (R2 = 0.76). The visible–short wave near infrared interactance spectroscopy technique is therefore not recommended for assessment of fruit firmness. The sound velocity technique is recommended for measurement of an index descriptive of the stage of ripening of mango, banana and peach fruit, although it does not assess the same character as a penetrometer reading.Crown Copyright © 2008 Published by Elsevier B.V. All rights reserved

Assessment of sugar and starch in intact banana and mango fruit by SWNIR spectroscopy

The prediction accuracy of models based on visible-short wavelength near infrared spectra (VIS–SWNIR; 500–1050 nm) collected from intact fruit using a partial transmittance optical geometry was considered for dry matter (DM) and total soluble solids (TSS) content of mesocarp tissue of banana (Musa acuminata, cv. Robusta) and mango (Mangifera indica, cv. Keitt) fruit. The DM content was modelled well across all stages of maturity for mango, with a cross validation correlation coefficient of determination (R2cv) > 0.75 and root mean square error of cross-validation (RMSECV) of 0.75, RMSECV 0.85) with skin colour (Hunter a and a/b) in the populations assessed. VIS–SWNIR is recommended for assessment of the ripening stage of mango and banana fruit and for assessment of DM in intact mango, but not banana fruit. The technique is also not recommended for assessment of TSS content across ripening stages of banana or mango fruit

Assessment of avocado fruit dry matter content using portable near infrared spectroscopy: Method and instrumentation optimisation

Avocado flesh dry matter content (DMC) is an index of eating quality of ripened fruit, and DMC is also related to fruit maturity, with a (cultivar dependant) minimum DMC recommended for harvest. Based on DMC variation within the fruit, the outer equatorial region of the fruit was chosen for optical and physical sampling. Three handheld near infrared spectrophotometers were compared for in-field non-invasive assessment of DMC, with the best results for prediction of independent sample sets obtained using an instrument employing an interactance optical geometry and the wavelength range 720−975 nm, with mean centred second derivative of absorbance spectra (e.g., correlation coefficient of determination, R2, for partial least squares regression model (PLSR) prediction of an independent test set of 0.71, compared to 0.37 and 0.31 for two reflectance geometry instruments). This performance difference to the reflectance geometry units was less marked for fruit with skin removed (e.g., prediction set R2 0.88 for the interactance geometry unit and 0.74 and 0.71 for the reflectance geometry units). PLSR model performance was examined for models based on cumulative combination of fruit populations across three growing seasons and four growing locations for a single cultivar model and a combined two cultivar model. Bias corrected root mean square of error of predictions stabilized in the third season at approximately 1.5 % dw/fw, with bias varying by approximately 1 %. The coefficients of the PLSR model stabilised as population size increased, making these values a useful guide to model stability. In-field use was demonstrated, tracking DMC of fruit on tree from between 14 and 27 % over several months to inform a harvest timing decision. Use on ripening fruit was also demonstrated. Tracking of known (tagged) fruit was recommended over assessment of randomly chosen fruit to reduce bias error in estimation of population DMC change

Assessment of sugar and starch in intact banana and mango fruit by SWNIR spectroscopy

The prediction accuracy of models based on visible-short wavelength near infrared spectra (VIS–SWNIR; 500–1050 nm) collected from intact fruit using a partial transmittance optical geometry was considered for dry matter (DM) and total soluble solids (TSS) content of mesocarp tissue of banana (Musa acuminata, cv. Robusta) and mango (Mangifera indica, cv. Keitt) fruit. The DM content was modelled well across all stages of maturity for mango, with a cross validation correlation coefficient of determination (R2cv) > 0.75 and root mean square error of cross-validation (RMSECV) of 0.75, RMSECV 0.85) with skin colour (Hunter a and a/b) in the populations assessed. VIS–SWNIR is recommended for assessment of the ripening stage of mango and banana fruit and for assessment of DM in intact mango, but not banana fruit. The technique is also not recommended for assessment of TSS content across ripening stages of banana or mango fruit

Variation in oil content and fatty acid profile of Calophyllum inophyllum L. with fruit maturity and its implications on resultant biodiesel quality

Variation in oil content and fatty acid profile (FAP) of Calophyllum inophyllum; a potential biodiesel feedstock species were studied at different maturity stages and biodiesel quality parameters were estimated based on the FAP. A steady increase in oil content was observed with maturity. Variation in palmitic (16:0) and linoleic (18:2) acids followed exactly opposite trends where palmitic acid content has decreased and linoleic acid content has increased 77 days after anthesis. Oleic acid (18:1) content has shown a steady increase. Stearic acid (18:0) content remained steady up to 68 days after anthesis and then felt slightly in 77 days after anthesis. Linoleic and eicosanoic acids were found to exist in low concentrations demonstrated very little compositional variation with fruit maturity. Estimated biodiesel parameters of all maturity stages were found to comply with industrial standards. Even though 48 days after anthesis had the most ideal FAP for biodiesel production, 77 days after anthesis is preferred point to harvest due to higher oil content

Assessment of potato dry matter concentration using short-wave near-infrared spectroscopy

The utility of short-wavelength near-infrared spectroscopy (over the wavelength region 750–950 nm), used in a partial transmittance optical geometry, was assessed as a means of estimating the dry matter concentration of potato tubers. The sampling optics did not involve contact with the sample, and could be used on a moving stream of product. A prediction accuracy of R2 (correlation coefficient of determination) of 0.85 with a root mean square error of prediction (RMSEP) of 1.52% for intact, whole tubers and R2=0.95 and RMSEP=0.50% for sliced tubers was achieved. We conclude that short-wavelength near-infrared technology using a partial transmittance optical sampling geometry can be a useful tool for rapid assessment of tuber dry matter concentration prior to processing