Firmness Tester Research Articles

The crucial evaluation indexes and relative measurement methods of edible value for fresh fruits and vegetables: A review

AbstractFresh fruits and vegetables (F&V), with their attractive sensory, flavors, and nutrients, occupy a highly valuable part of people's daily diet. Plenty of postharvest studies have focused on the effects of preservation techniques on the edible quality and nutrients of F&V. Therefore, it is crucial to summarize the research methods of those quality and nutritional indicators for F&V. This work reviews the measurement and analysis methods on these parameters of postharvest F&V which have been validated by numerous publications, including firmness, sugars/acids, pigments, phenols, etc. Importantly, some instruments are involved in these experimental methods, such as firmness tester, spectrophotometer, gas chromatograph, and so on. Furthermore, some promising analysis methods in the past decades combined with advanced technologies are also raised in our discussion. This work is expected to provide guidance and normative significance for future research on quality assessment, preservation, and processing characteristics of F&V.

Combining High-throughput Phenotyping and Multivariate Analysis to Assess Fruit Quality Traits in Southern Highbush Blueberry (Vaccinium corymbosum Interspecific Hybrids) Germplasm Collection

Breeding blueberry cultivars with enhanced fruit quality requires simple, accurate, and cost-effective assays to select individuals from segregating populations. In this study, berry diameter, berry weight, firmness, pH, total polyphenol, total acids, D-glucose, D-fructose, total glucose, and total sugar content were quantified in 188 southern highbush blueberry selections and cultivars over 2 years. Significant variation between years, genotype, and year × genotype interaction was detected for all traits. Glucose and fructose were the predominant sugars, and they were in a range of 32.14–64.72 and 28.61–69.63 mg/mL, respectively. Total sugars content ranged from 62.22 to 131.15 mg/mL. Correlation analysis showed a strong positive correlation between total sugar content measured with the discrete analyzer and total soluble solids assessed as Brix (r2 = 0.96). In addition, glucose, fructose, and total glucose showed high and positive correlation between them and with the total sugar content. The titratable acidity was positively correlated with total acids (r2 = 0.60) and strong positive correlation between berry diameter and berry weight (r2 = 0.94) was detected. Principal component analysis (PCA) showed that PC1 explained 44.9% of the variation and the major contributing traits for diversity were D-fructose, D-glucose, total glucose, and total sugars. PC2 accounted for 21.2% of the variation and was mainly attributed to berry weight and diameter. Cluster analysis showed that the blueberry genotypes fell into two major groups. Cluster-I comprised genotypes with the highest amounts of total acids, pH, polyphenol, D-glucose, D-fructose, total glucose, and total sugar, whereas Cluster-II has genotypes with distinctly lower amounts of tested compounds and larger berries. Information obtained from this study is critical to identify superior genotypes for future crosses and advance evaluation. In addition, the firmness tester and discrete analyzer used in this study were invaluable in improving the efficiency and precision of phenotyping.

Benchmarking of techniques used to assess the freeze damage in potatoes

In this study, benchmarking of methods used for assessing freeze damage in potatoes was carried out. Initially, the samples were frozen by subjecting them to three different temperatures (i.e. at –18 °C, − 30 °C, and at −74 °C). Then, different analytical techniques comprising of focused methods (i.e. cryo-Scanning elctron microscopy-cryo-SEM, confocal laser scanning microscopy-CLSM) and global methods (i.e. texture analysis, low field nuclear magnetic resonance (NMR), exudate loss and colour change) were used to assess the impact of the freezing treatment from the different point of view addressed by each method. As a result, each of these methods were able to distinguish significantly fresh samples from the frozen-thawed samples. Focused methods like cryo-SEM and CLSM methods could differentiate the impact of all three different protocols. Meanwhile, texture analysis (including conventional method and novel method based on a touchless laser puff firmness tester), NMR and exudate loss could only determine the quality difference between −18 °C and − 74 °C freezing conditions. Colour analysis was found as an inappropriate parameter for comparing the three freezing protocols. Among all analytical techniques, cryo-SEM provides the most authentic information about the product as the analysis is performed in frozen state, while for other techniques the product is thawed prior to analysis.

Mango Firmness Modeling as Affected by Transport and Ethylene Treatments.

More and more, tropical fruit are subjected to accelerated ripening at receiving markets until “ready to eat.” We propose a kinetic model that incorporates the effects of temperature and ethylene on the firmness behavior of “Keitt” and “Kent” mangoes. Stiffness of individual mangoes, as measured by the acoustic firmness tester, was measured repeatedly over time. The firmness model assumes fixed levels of ethylene, established after the climacteric peak, that steadily induces production of softening enzymes that subsequently denaturalize. The initial level of these enzymes is assumed to be zero due to either the tree factor for freshly harvested mangoes, or due to chilling injury for reefer transported mangoes. The kinetic parameter set for “Keitt” mangoes was estimated based on a Spanish batch, freshly harvested and ripened under dynamic temperature scenarios, combined with a reefer transported Brazilian batch stored at four constant temperatures. Firmness data from reefer transported batches, from Brazil, Ivory Coast and Mali, stored at four constant temperatures were used to estimate a set of kinetic parameters for the “Kent” mangoes. Only a partial set of “Kent” kinetic parameters could be established due to the often already advanced stage of softening at the time of arrival. The effect of ethylene was investigated by applying external ethylene levels, varying from 0 to 100 μL L−1. The effect of external application of ethylene was modeled by estimating EF, the ethylene factor, being a reflection of the internal ethylene level and ethylene sensitivity. The effect of ethylene application on softening was sometimes huge. For an Israeli “Keitt” batch a fifty times higher EF was found when the firmness behavior of low- (without ethylene application) and high temperature (with ethylene application) stored sub-batches were compared. However, this effect was sometimes also small, especially for reefer transported mangoes. For commercial application, a reliable prediction of the time until “ready to eat” is not possible because of the current inability to assess EF. Nevertheless, the proposed model described mango softening accurately, irrespective of the sourcing area and includes the effects of storage temperature and ethylene application.

Non-destructive measurement of fruit firmness to predict the shelf-life of ‘Hayward’ kiwifruit

Kiwifruit are commercially harvested when mature but firm. The length of the softening process in shelf life (SL) till the fruit becomes edible depends upon several pre-harvest and postharvest factors, such as harvest maturity, environmental conditions (temperature, relative humidity, composition of the atmosphere) and duration of cold storage. The ability to predict the time till softening would be of considerable commercial value, but to date there is no reliable method to do so. We assessed the suitability of the non-destructive measurement of fruit firmness with the Sinclair IQ Firmness Tester (SIQ) to predict the time required for adequate fruit softening for consumption. ‘Hayward’ kiwifruit were harvested at commercial maturity and stored in either regular air (RA) or controlled atmosphere (CA), with or without pre-storage 1-MCP treatment. Fruit softening was monitored during shelf-life at 20 °C by finger pressure, SIQ and with a FTA penetrometer. Highly significant correlations were obtained between SIQ and both firmness assessments. The linear decline in firmness measured by the non-destructive SIQ enables us to construct models that can predict the time till softening, based on the SIQ value at removal from storage under different conditions.

Inter-correlation of apple firmness determinations and development of cross-validated regression models for prediction of sensory attributes from instrumental and compositional analyses

The texture of apples is paramount for determining fruit quality. This research explored the correlations among firmness determinations from the Sinclair iQ™ Firmness Tester (SiQ™), the Aweta Acoustic Firmness Sensor (AFS), and eight measurements from the Mohr Digi-Test-2 (MDT) instrument. Assessments were conducted on a collection of nine apple cultivars (Ambrosia, Aurora Golden Gala™, Honeycrisp, Fuji, Imperial Gala, McIntosh, Pink Lady™, Silken, Salish™), with a broad range of firmness values, in each of two years. Sensory analysis of the apples was conducted using a semi-trained panel (n = 10) to evaluate crispness, hardness, juiciness and skin toughness, in quadruplicate at two testing dates, providing eight data points per cultivar per year. Inter-correlations of the instrumental firmness determinations (SiQ™, AFS, MDT) revealed that most values were highly correlated with one another (r > 0.500 n = 72). This suggested that the instruments were tracking similar, but not identical, underlying characteristics. Multiple regression models were developed using the 2016 data to predict the sensory attributes from the instrumental and compositional (titratable acidity, soluble solids concentration, absorbed juice) analyses. Models with the highest R2 were cross-validated using the 2015 data. Accuracy of these models was evaluated using R2 and prediction standard errors (PSEs) - an index quantifying the difference between the predicted and actual values. In general, simple 1- and 2-variable models satisfactorily predicted hardness and crispness, with the R2 values ranging between 85 and 89%, while more complex non-linear models were required to predict juiciness and skin toughness. Correlations coefficients reported in this research allow for interconversion of experimental firmness data, as determined by the SiQ™, AFS and MDT. Regression models predicting hardness, crispness and juiciness from instrumental/compositional analyses, revealed that the quality factor (QF) variable was particularly important for estimation of textural characteristics. Therefore the MDT, among the instruments evaluated, was the instrument of choice for quality assessment of apples. Since cross-validation of the models accounted for a high proportion of the variance (70–82%) in a new data set with small PSEs (2.67–6.36) (on a 100-unit scale), the developed models were appropriate for estimating the apple textural attributes.

Study on the quantitative measurement of firmness distribution maps at the pixel level inside peach pulp

Firmness is a major quality index for peaches and many other fruits. Because firmness varies among different parts of the pulp, maps of the firmness distribution inside peach pulp can be used as an important indicator for deeply understanding and evaluating the ripening process of fruit and for further optimizing planting patterns and postharvest storage strategies. However, the commonly used Magness-Taylor firmness tester cannot provide the firmness distribution at the pixel level, because it measures only one or a few sampling points where the probe is located. In this study, a laboratory-based hyperspectral imaging system in the wavelength range of 380–1030nm was developed to detect and visualize the firmness distributions for different cross sections of peach pulp. Two calibration methods based on partial least squares regression (PLSR) and least squares support vector machines were applied to establish firmness calibration models. In addition, the successive projections algorithm, uninformative variable elimination, and competitive adaptive reweighted sampling (CARS) were applied separately to select the optimal wavelengths to improve the models’ accuracy and robustness. CARS-PLSR without preprocessing was determined to be the best model for determining the firmness of peach pulp, yielding prediction results with a correlation coefficient of 0.852 and a residual predictive deviation of 1.739. Using this model, the firmness distributions for different cross sections of peach pulp were quantitatively visualized at the pixel level. The firmness histogram revealed the existence of a wide range of firmnesses inside peach pulp. The overall results demonstrated that hyperspectral imaging shows great potential for visualizing the spatial variations in the firmness distribution inside peach pulp at the pixel level, which can provide a more detailed understanding of the ripening process of peach fruits during the preharvest and postharvest periods.

Comparison of firmness meters for measuring ‘Hass’ avocado fruit firmness

The temporal passage of fruit through the supply chain and the selection of consumable fruit by shoppers depend primarily upon fruit firmness. Traditionally, fruit firmness measuring methods, like Effegi and conical probes, are relatively inefficient and destructive. Simple, accurate and non-damaging methods of measuring fruit firmness are ideally required to help assure eating quality to the consumer without fruit wastage. The firmness of 'Hass' avocado fruit at a range of ripening stages was measured with the various different firmness measuring techniques of the Sinclair iQ Firmness Tester (SIQFT), the Electronic Firmometer (EF), the Analogue Firmness Meter (AFM) and hand squeezing. Measurements were made by each method at different points on the same fruit. Destructive bruise assessment was performed 48 h later, thereby allowing sufficient time for fruit to express any bruising resulting from the act of firmness measurements. Non-linear relationships were determined between fruit firmness values measured with the different techniques. The adjusted R2 for the relationship between the SIQFT and the EF was 91.6%. For the SIQFT and the AFM, the adjusted R2 was 73.7%. It was 77.7% for the SIQFT and hand squeezing. A significantly (P<0.05) high incidence of bruising was associated with firmness assessment by the EF as compared with either the SIQFT or the AFM. Among the methods compared, the SIQFT was non-damaging compared with the EF and relatively efficient for measuring the firmness. This instrument merits consideration as a quality control tool of choice in 'Hass' avocado supply chains.

Characterizing the Frequency Distributions for Fruit Firmness of Sweet Cherry Cultivars

Fruit firmness is among the most important characteristics for the quality of sweet cherries. However, little has been published on its underlying frequency distribution. This research was undertaken to examine the firmness distributions (n = 48) from six cultivars [Sandra Rose, Summit, Lapins, Skeena, Sumtare (Sweetheart™), and 13S2009 (Staccato™)], two field treatments [with or without gibberellic acid (GA)], two storage times (0 and 7 days), and two growing seasons (2013 and 2014). Fruit was sampled (n = 300) at optimal maturity and firmness was evaluated using the FirmTech2 Fruit Firmness Tester. Firmness distributions were examined using descriptive statistics: mean, median, standard deviation (sd), minimum, maximum, range, skewness, and excess kurtosis. Nonnormality was assessed using skewness and kurtosis test statistics. Exponential models were fitted to the ascending and descending portions of the distributions and the proportion of “too soft” (percentage &lt; 2.56 N·mm−1) and “too hard” (percentage &gt; 4.71 N·mm−1) fruit was determined. A relatively high proportion of distributions were nonnormal (16/24 to 18/24), either skewed, peaked, or both. While most skewed distributions were skewed negatively, with a higher proportion of softer fruit, the distributions for ‘Sandra Rose’ were skewed positively, with a higher proportion of firmer fruit. Principal component analysis (PCA) showed seasonal, cultivar, treatment, and storage effects among three subsets of cultivars with differing characteristic firmness. The softer early-harvest cultivars (Sandra Rose and Summit) had a higher proportion of “too soft” fruit. GA and storage treatments increased mean firmness and reduced the proportion of “too soft” fruit. The firmer late-harvest cultivars (Skeena, Sumtare, and 13S2009) had a small proportion of “too hard” fruit (0% to 19.3%). The work gained insight into the nature of the firmness distributions for sweet cherries and the type of statistics that are most appropriate for analyzing the data.

Assessment of eggplant firmness with accelerometers on a pneumatic robot gripper

A pneumatic robot gripper capable of sorting eggplants according to their firmness has been developed and tested. The gripper has three fingers and one suction cup. Each finger has an inertial sensor attached to it. One of the fingers adapts to and copies the shapes of eggplants when the jamming of its internal granular material changes from soft to hard. The other fingers adapt to the shape of the eggplant with the use of extra degrees of freedom. Specific software acquires and processes the information obtained with the inertial sensors and generates 16 independent variables extracted from the signals. A total of 234 eggplants were selected and tested on the same day with the robot gripper, during the pick-and-place operation, and with a destructive firmness tester. The non-destructive parameters extracted from the gripper finger accelerometers were used to build and validate a partial least square model, with a calibration regression coefficient of r=0.87 and a high prediction performance (r=0.90). Furthermore, from the results of the paper, it has been seen that the procedure can be simplified by using only two non-destructive impacts and one uniaxial accelerometer to assess eggplant firmness. The non-destructive assessment of firmness while grasping agricultural products in pick-and-place operations could be implemented in many prehensile pneumatic robot grippers. This technique could mean an important advance in the hygienic postharvest handling of fruits and vegetables.

Determining the optimum firmness for sweet cherries using Just-About-Right sensory methodology

This study evaluated Just-About-Right (JAR) ratings of untrained panellists for whole sweet cherry (Prunus avium L.) crispness and flesh firmness. Cherries from 17 different cultivars and breeding selections representing a wide range of fruit firmness were harvested over the course of the cherry maturity season in 2011 and 2012. The firmness of each cherry (n=183–500 per sample) was measured with the FirmTech 2 Fruit Firmness Tester, and the fruits were sorted into 19 firmness categories, of 0.20N increments, ranging from 1.58 to 5.69N. Untrained panellists (n=48) assessed whole cherry crispness and flesh firmness using a 7-point JAR scale from ‘much too soft’, through ‘just about right’, to ‘much too firm/crisp’. Four to eight firmness categories were evaluated per session in a series of 10 sensory sessions. Linear regression was used to model the relationship between JAR ratings and analytical firmness values, for whole cherry crispness (r2011−122=0.75) and flesh firmness (r2011−122=0.75). Crispness and firmness were highly correlated in both years (R2011=0.99, R2012=0.99). Frequency distributions of JAR ratings identified the proportion of responses at each rating (1–7), within each firmness category. The “acceptable” firmness range was calculated to be 2.52–4.75N from JAR flesh firmness scores of between 3 (‘slightly too soft’) and 5 (‘slightly too firm’), respectively. Cherries with measured firmness values between 2.56 and 4.71N were “acceptable” to 72.9–91.7% of panellists. The work established the relationship between sensory and analytical evaluations in order to develop guidelines for acceptable sweet cherry fruit firmness to use in research on cultural practices and the selection of new cultivars.

FORCED-AIR COOLING REDUCES 1-MCP APPLICATION DURATION ON PLUMS (PRUNUS SALICINA LINDL.) WITHOUT REDUCING EFFECTIVENESS

FORCED-AIR COOLING REDUCES 1-MCP APPLICATION DURATION ON PLUMS (PRUNUS SALICINA LINDL.) WITHOUT REDUCING EFFECTIVENESS

A novel instrument to delineate varietal and harvest effects on blueberry fruit texture during storage

Firmness is an important quality index for blueberries. It is the major factor that determines consumer acceptability, storability and resistance to injury and diseases during storage and fresh marketing. Blueberry cultivars vary in their firmness, with southern highbush cultivars usually softer than Rabbiteye blueberries. In this study, varietal and harvest effects on blueberry firmness were measured by the Firmtech II and laser air-puff instruments. This was the first time that the laser air-puff, a non-contact food firmness tester, had been used for firmness testing of small fruit, such as blueberry. Two southern highbush cultivars (Sweet Crisp and Emerald) and two Rabbiteye cultivars (Vernon and Savory) were used for varietal effect measurement, while a Rabbiteye cultivar (Premier) that was both machine and hand harvested was used for harvest effect observation. Fifty berry samples per replicate and four replicates were tested by two instruments at harvest and after 7, 14, or 21 days of storage. The laser air-puff tester successfully delineated the difference in firmness due to cultivar characteristics and harvest methods, as well as the firmness loss over 21 days of postharvest cold storage (4 °C). The firmness index derived from the laser air-puff tester achieved a significant correlation with the firmness values measured by the Firmtech (R(2)=0.80). A new texture index, springiness, was developed from the laser air-puff, which largely reflects the varietal differences in elasticity of fruit. This study demonstrated the efficacy of the laser air-puff instrument for blueberry firmness measurement. This non-contact instrument not only provides an alternative method of firmness measurement, but also offers a new index for fruit elasticity evaluation and better texture evaluation for blueberries.

CRITERIA FOR PICKING PLUMS (PRUNUS DOMESTICA L.) AT THE OPTIMUM MATURITY STAGE FOR THE FRESH FRUIT MARKET

Norwegian plum growers and marketing organizations wish to improve the status of plums in the fresh fruit market. To assure only high quality plums of uniform quality and ripeness are marketed, grading machines have been introduced at the packing houses. However, the pickers have to be trained to judge the maturity stage of the plums when picking. Methods, to help the pickers and the marketing people have a similar understanding of maturity, have been tested. Colour charts with pictures showing different maturity stages of the major plum cultivars grown in Norway, have been developed. The best way to judge the maturity of fruit of the plum cultivar 'Jubileum', however, is by firmness. The portable Durofel firmness tester is well suited for training pickers to judge maturity in 'Jubileum' plums. Threshold values for firmness related to different maturity stages, as measured by the Durofel firmness tester, have been established.

Changes of Physical and Chemical Traits of Roots of Different Carrot Cultivars Under Cold Store Conditions

Changes of Physical and Chemical Traits of Roots of Different Carrot Cultivars Under Cold Store Conditions The objective of this three-year study, performed in 2006-2008, was to determine the influence of long-term cold storage on some physical and chemical parameters influencing nutritive and biological value of carrots of different storage root colours and shapes. Eight carrot cultivars were chosen for the experiment: Perfekcja, Interceptor F1, Vita Longa, Nebula F1, Purple Haze F1, Deep Purple F1, Mello Yello F1 and White Satin F1. Quality parameters of the carrots, including CIE L*a*b* colour parameters of the whole roots and juice, root firmness (with HPE firmness tester), dry matter, soluble solids, sugars, total phenolics, total carotenoid, antioxidant activity of the whole roots and juice (with DPPH method), were determined directly after harvest and after 6-month storage of the roots at 0-1°C and 97% RH. The results showed significant influence of the storage on most of the quality traits of the roots, particularly colour, firmness, soluble solids and phenolics content. These changes were also related to the cultivars. Storage of the carrot roots caused a decrease in their firmness. Soluble solids and sugars content showed a tendency to increase during the storage period. Storage of the carrots resulted in decreasing nitrates content and increasing phenolics and carotenoid compounds content in the roots. Antioxidant activity of carrot juice showed small increasing tendency resulting from the storage of the roots, but antioxidant activity of the roots decreased.

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EVALUATING BERRY FIRMNESS AND TOTAL SOLUBLE SOLIDS OF NEWLY RELEASED HIGHBUSH BLUEBERRY CULTIVARS

Berry firmness and total soluble solids (TSS) of three newly released highbush blueberry cultivars ('Draper', 'Liberty', and 'Aurora') were evaluated in the 2005 growing season against three popular commercial highbush cultivars ('Duke', 'Bluecrop', and 'Elliott') grown in the Northwest during and after cold storage at temperatures between 1 to 4'C. These experiments were conducted using berries from the first pick in a variety evaluation plot at the North Willamette Research and Extension Center. Berry firmness was determined with a FirmTech II firmness tester. After cold storage durations of 1, 2, and 3 weeks, berries were taken out and acclimated at room temperature. Berry firmness and TSS then were determined every three days until berries were considered unmarketable. At week 1, 'Draper' and 'Duke' had the highest firmness in the first three days, and 'Elliott' and 'Liberty' had the lowest firmness during the 12 days sitting at room temperature. 'Liberty' had the highest TSS among all cultivars. At week 2, 'Duke' had the highest firmness while 'Elliott' had the lowest firmness during the 12-day period. For TSS level, 'Bluecrop' was the lowest and 'Elliott' and 'Liberty' were the highest. At week 3 during the 12day test period, the cultivars with the highest and lowest firmness were 'Duke' and 'Elliott' respectively. TSS in 'Liberty' was still among the highest, and 'Bluecrop' and 'Liberty' had the lowest TSS level among all cultivars. If shelf-life is measured by berry firmness at room temperature, the shelf-life of 'Duke' and 'Draper' were very similar. 'Aurora' had a shelf-life better than 'Elliott'; however 'Liberty' had a shelflife no better than 'Bluecrop'.

Eating quality standards for apples based on consumer preferences

Consumer acceptance and liking of ‘Red Delicious’, ‘Gala’, ‘Fuji’, ‘Golden Delicious’ and ‘Braeburn’ apples were assessed in separate trials for each cultivar. Apples were sorted into six or eight categories using a mixture of destructive and non-destructive measurements of texture and taste. Non-destructive measurements included an acoustic texture measurement (Aweta Acoustical Firmness Sensor, AFS), Sinclair Internal Quality Firmness Tester (SIQ-FT) and Near Infra-Red (NIR) spectroscopy. Destructive measurements included firmness by puncture tests, soluble solids content (SSC), titratable acidity (TA) and pH. More than 100 US consumers evaluated the fruit at each evaluation date, with individuals tasting an apple from each of the 6–8 texture/taste categories. Preference mapping and other statistical methods reaffirmed that firmness is the primary edible quality factor that contributes to consumer acceptance and preference in the USA. High SSC and/or TA may result in further improvements in consumer acceptance, but usually only in apples that are firm. While there is a general positive trend for increasing preference with firmness, some consumers will prefer softer apples, and some will dislike the firmest apples. Using combinations of firmness SSC and TA, it was possible to identify categories of apples that achieved consumer acceptability levels from 43% to 90%.

A Preliminary Study on Classification of Mango Maturity by Compression Test

A preliminary study toward the design of a firmness tester suitable for mango maturity classification was conducted through an experiment consisting of two parts: (1) probe selection, followed by (2) evaluation of the selected probe in mango maturity classification using a texture analyzer. This resulted in a technique based on the firmness of the fruit measured by a compression test with a maximum force of 3N using a 5mm diameter spherical stainless steel probe. This technique demonstrated the possibility of classifying the maturity of mangoes into two different stages, i.e., 60% and 80% of full ripeness. However, it could not detect the difference between 60% and 70% of full ripeness or between 70% and 80% of full ripeness.

Relationship between nondestructive firmness measurements and commercially important ripening fruit stages for peaches, nectarines and plums

Fruit firmness measurement is a good way to monitor fruit softening and to predict bruising damage during harvest and postharvest handling. Ripening protocols traditionally utilize a destructive penetrometer-type fruit firmness measure to monitor ripening. Until recently, methods of assessing fruit texture properties nondestructively were not commercially available. The nondestructive Sinclair iQ™ firmness tester was investigated to monitor ripening and predict bruising susceptibility in stone fruit. This work was carried out on four peach, three plum, and five nectarine cultivars over two seasons. The correlations between destructive and nondestructive firmness measurements were significant ( p-value = 0.0001), although too low for commercial applications as they varied from r 2 = 0.60–0.71 according to fruit type. Using a different approach, the relationship between destructive and nondestructive firmness measures was characterized in terms of segregating these fruit according to their stages of ripening. This was done by using discriminant analysis (66–90% agreement in ripeness stage classification was observed in validation tests). Discriminant analysis consistently segregated nondestructive firmness measured fruit into commercially important classes (“ready to eat”, “ready to buy”, “mature and immature”). These represented key ripening stages with different bruising potentials and consumer acceptance. This work points out the importance to relate nondestructive measurements directly to important commercial physiological stages rather than to correlate them with the current standard penetrometer values. Thus, destructive and nondestructive firmness measurements can be directly used to identify the stage of ripeness and potential susceptibility to bruising during postharvest changes. Further work is recommended to evaluate the performance of this nondestructive sensor in segregating fruit according to their stage of ripeness under packinghouse or processing plant conditions.