Nondestructive Monitoring of Ripeness in Mango Cultivars by Acoustic Resonance Spectroscopy

Abstract

Mango cultivars, i.e. Neelam, Alphonso, Banganapalli, Totapuri, and Mallige were subjected to acoustic resonance spectroscopy at unripe, semi-ripe, and ripe stages, as well as in case of ripe bruised fruits. The Neelam variety of mango was screened for internal infestation by means of acoustic spectroscopy. Acoustic data on dominant and first frequencies, damping ratio, and firmness indices were obtained and correlated with mechanical penetration tests as well as a/b values of the tristimulus color profile. The correlations between mechanical tests and acoustic parameters varied from 0.81 to 0.933. However, damping ratio and dominant frequency along with firmness index showed a better correlation with penetration test vis-à-vis first frequencies. The correlations between acoustic parameters and a/b tristimulus color values showed lesser extent and significance of correlations ranging from 0.416 to 0.667. The correlations were found to be positive or negative depending on the specific acoustic parameters. However, in the case of internally infested Neelam, the drop in the first frequency was found to be a significant acoustic indicator. Nondestructive quality monitoring by acoustic spectroscopy was found as such to be consistent as far as the tissue softening and bruising are concerned.

Keywords:

• Mango
• Acoustic spectroscopy
• Frequency
• Damping ratio
• Firmness index
• Texture
• Tristimulus color
• Bruising
• Infestation

INTRODUCTION

Among tropical fruits, mango has a major commercial value with significant demand in the global market. India is one of the major producers of mango, with rich diversity in terms of cultivars. Several of these varieties have export potential, as they are endowed with excellent sensory attributes with optimal texture, color, and flavor characteristics without being excessively fibrous. However, the quality monitoring techniques in the packing houses are limited and mostly pertain to specific gravity[1] or are dependent on subjective maturity indices in the form of maturity periods.[2] The specific gravity method in widely used as a batch/quality monitoring method by dumping the fruits in water tanks followed by individual packing of floaters and sinkers.[3] The online sorting as such is based on size variation and visual observation of discoloration, infestation and mechanical damages necessitating the need for suitable objective and nondestructive means of quality monitoring. Acoustic excitation methods offer excellent non-intrusive means for post harvest quality monitoring and also find their due place in levitation studies during food processing.[4] Acoustic instrumentation was widely used for nondestructive quality control of several fruits. Apart from maturity aspects, specific quality aspects related to the hollow heart in watermelons,[5] the extent of mealiness in apples,[6] chilling injury,[7] and puncture injury susceptibility of tomatoes[8] were studies extensively using appropriate acoustic testing measures. The reports on nondestructive quality monitoring of mango are, however, limited. The techniques include electron spin resonance for the detection of irradiated mangoes.[9] X-ray imaging has drawn considerable attention for the detection of seed weevil infested mango.[10] The development of spongy tissue has also been successfully detected in Alphonso mangoes by x-ray imaging techniques.

Magnetic resonance imaging (MRI) has also been used for the detection of heat injury during thermal treatments imposed on the mango fruits for the control of infestation.[11] X-ray absorption was used as an index to detect quality of mangoes as related to their density, moisture content, soluble solids, titratable acidity, and pH.[12] Schmitovitch et al.[13] have reported high correlations between near infra red spectrometry and firmness from penetration test, apart from soluble solids in Tommy Atkins variety of mangoes.

The development of spongy tissue had been a major problem in Alphonso mangoes and x-ray imaging technique was successfully applied for detection of the same as an online process apart from the internal infestation, a frequent problem in mangoes.[14] However, x-ray imaging or absorption techniques are cost intensive and highly vulnerable to maintenance problems. Therefore considerable attention has been drawn towards acoustic resonance spectroscopy for the evaluation of mango quality, and attempts have been made to establish correlations between acoustic resonance data and fruit firmness, as well as other quality parameters.[15] Apple under cold storage received the maximum attention in terms of use of acoustic data for arriving at comprehensive understanding of rheological or textural parameters.[16]

The present study was aimed at nondestructive evaluation of mango cultivars, i.e., Alphonso, Neelam, Banganapalli, Totapuri, and Mallige, grown extensively in India, and the varieties are as such preferred in global market. Bruised or internally infested mangoes have also been subjected to acoustic spectroscopy with piezo electric film sensors for the quality monitoring. The study include screening of different types of fruit beds and acoustic excitation methods to obtain consistent acoustic profiles suitable to mango fruits with characteristic heterogencity in the tissue systems and single large seed at the center. Efforts were made to minimize scatter of acoustic profiles using optimized experimental setup suitable for the selected cultivars of mango.

MATERIALS AND METHODS

Fruits and Grading

Five varieties of farm fresh mango fruits with a specific gravity of 1–1.02 were procured as 50 kg lots for each variety. Preliminary sorting was carried out by eliminating fruits with physical blemishes, discolorations, infections and deformities. Further grading of the mangoes (unripe) was carried out based on average weight per unit within specified ranges for each variety. The weight ranges followed for the grading were 320–350 gms (Cv. Totapuri), 480–510 gms (Cv. Mallige), 490–520 gms (Cv. Banganapalli), 220–250 gms (Cv. Alphonso), and 160–190 gms (Cv. Neelam). The experimental fruits without the visual blemishes were selected within the respective mass ranges.

Ripening Process

Fruits of the five different cultivars were subjected to ripening after pretreatment with 2-chloro-ethyl-phosphonic acid solution (100 ppm) for 5 minutes and surface moisture was removed by keeping the fruits under dehumidified chill air flow (8–10°C). The fruits were packed in vented and telescopic card board boxes accommodating 4 kg fruits in each. The boxes were kept in a humidity controlled incubator at 30°C at a relative humidity of 90. Each variety consisted of 10 boxes and samples were drawn at unripe, semi-ripe, and ripe stages after predetermined periods of storage. The predetermined storage periods to attain semi-ripe and ripe stages for different cultivars were Cv. Neelam 4 and 7 days, Cv. Alphonso 5 and 8 days, Cv. Banganapalli 7 and 11 days, Cv. Totapuri 6 and 10 days and, Cv. Mallige 7 and 10 days respectively.

The ripening stages were defined based on a sensory index on a 1–9 point hedonic scale covering sensory attributes i.e. texture, color, aroma, and taste. A panel consisting of 10 trained panelists was used for the sensory evaluation carried out under white light at 20°C. Samples in whole and precut form were coded with three digit numbers and served randomly for evaluation in terms of color, aroma, taste, and texture to determine the stage of ripeness. The unripe, semi-ripe, and ripe fruits showed an ascending rate of scoring where the average overall scores were 1–3 for unripe, 4–6 for semi-ripe, and 7–8 for ripe fruits. Fruits at the 3 ripening stages were drawn at the specified intervals for acoustic, color and mechanical analysis.

Acoustic Analysis

A dynamic excitation method was adopted with suitable modifications.[17] The experimental setup included a flat bed for positioning the fruit horizontally and consisted of soft polyethylene foam padding material in the form of sheet to facilitate free vibrations of the fruit (Fig. 1). The Piezo electric film sensors composed of poly vinylidene fluoride (PVDF), and mechanical impulse hammer with a semi-spherical rubber tip, preamplifier, and a dynamic signal analyzer to yield fast fourier transforms. The piezo electric film sensor made of PVDF measuring 4 × 4 cms was fixed to the fruit bed using a silicone grease coupler. The fruit bed was placed on a polyurethane mat to minimize the background resonance. The acoustic setup involved amplifications of the swept signals arising out of the dynamic excitation of the fruit resulting in fourier transform power spectra in the frequency, as well as time domains. The Piezo electric sensor was composed of poly vinylidene flouride coated with 2 thin layers of conductors[18] which were bound to soft polyethylene foam padding to allow free vibrations of the fruit. The sensor was positioned at 180°C to a mechanical impulse device. The fruit was firmly held to the film sensor at 180° using a silicone grease coupler. The impulse hammer was used to excite the fruit with equal energy density at each frequency up to 1000 Hz and averages of ten sonic spectra were considered for measurement. The preamplified acoustic signals from the piezo electric sensor were analyzed in a dynamic signal analyzer (Perkin-Elmer) and power spectra were obtained depicting the acoustic profile in frequency as well as time domains. The most conspicuous frequency viz-a-viz other frequencies in each spectrum were taken as the dominant frequency. Whereas the first frequencies are considered in serial order in each spectrum.

Figure 1 Schematic diagram depicting the sonic experimental setup.

Damping Ratio and Firmness Index

The conventional half-power technique was adopted for calculating the damping ratio of the dominant frequencies in the different power spectra obtained for different cultivars at the three different stages of ripening as well as under bruised conditions. The two half power resonance frequencies W₁ and W₂ were identified for the dominant resonance peaks and the damping ratio was calculated by the expression given below.[17]

(1)

where W_o is the frequency peak magnitude.

Damping ratio is such described as a ratio between substrated value of half power frequencies of the dominant frequency with double the value of the dominant frequency. The modified firmness index is taken as a product of square value of dominant frequency and two third of the fruit mass following the formula[17] given below:

(2)

where dm represents dominant frequency and m represents mass of the fruit.

Tristimulus Color

The tristimulus color profiles were obtained using a color measurement meter (Data Lab, Silvasa) calibrated by using standard white ceramic tile. The data pertaining to L, a and b values (L = 77.04, a = −1.4, b = 21.3, a/b = −0.07) were recorded using a D65 bulb as the illumination source at an angle of 45° with an observation interval of 2 nm. The values were expressed on hunter scale using an inbuilt software Chromaflash (Data Lab, Silvasa).

Mechanical Penetration Test

Cheeks of mango cultivars were obtained by vertical slicing of the fruit samples to minimize the effects of seed interference in the Penetrometric texture measurements (2.5/0.8 cms). Six fruits in each lot from each ripening stage of different cultivars were subjected to mechanical penetration measurements. Each half of fruit was kept on a hard and flat surface, and the cylindrical probe (0.8 cm dia and 2.5 cm length) of the penetrometer (Effigi, Italy) was driven completely in a vertical position with a thumb placed at the top of the gauge. Each fruit was subjected to a total of twelve punctures with six punctures in each half covering the proximal, middle, and distal portions of the fruit. The penetration force was measured in Newtons and the mean value for twelve punctures were considered. The same process was repeated for all six fruits in each sample.

Selection of Bruised and Infested Fruits

All the 5 cultivars were subjected to acoustic analysis of bruised fruit at the unripe stage. The method of bruising was carried out by causing physical stress[19] by dropping from a height of 2 m thrice, holding the fruit in different positions. The bruised fruits were also subjected to ripening at 30°C after pretreatment with ethrel (100 ppm). The same fruits were subjected to acoustic analysis two days before the specified duration for attaining ripeness for individual cultivars, as the bruised fruits showed accelerated ripening as compared to the healthy fruits. Internally infested fruits of cultivar Neelam were subjected to acoustic study after sorting out the infested fruits at the semi-ripe stage by means of perforation on the surface of the fruit caused by the fruit fly. The internal infestation of the fruits was later on confirmed by splitting the fruits after the acoustic study.

Statistical Analysis

The data obtained during acoustic analysis in the form of resonance frequencies, damping ratio and firmness indices along with mechanical penetration test and tristimulus color profile were subjected to analysis of variance for the individual cultivar at different stages of ripening as well as under bruised condition.[20] Correlation coefficients were calculated first between the acoustic parameters and penetration test, as well as a/b values of color to draw conclusions with regard to the relationships. The “a” value represents redness to greenness (+ 100 for red and –80 for green), and “b” represents yellowness and blueness (+ 70 for yellow and –80 for blueness).

RESULTS AND DISCUSSION

Quality monitoring of mango is an important feature of packing house operations. Visual grading or mechanical size grading fail to eliminate internally defective fruits in terms of ripeness/texture, minor bruises or internal infestation. Nondestructive means of quality control by means of acoustic spectroscopic methods offer advantages in making rapid sorting as an online process.

Resonance Frequencies

The acoustic study carried out showed well marked resonance profiles in the frequency domains. It was observed that the different cultivars selected for the study showed a progressively declined tendency in the magnitude of dominant, as well as first frequencies (Table 1). However, the number of frequencies were found to increase or decrease with advancement in ripening. The cultivars Neelam, Alphonso, and Banganapalli recorded emergence of additional frequencies with the advancement in ripening whereas, the cultivars Totapuri and Mallige recorded depletion in number of frequencies from semi ripe to ripe stage. A typical power spectrum in the frequency domain pertaining to Cv. Neelam is shown as Fig. 2. The first frequencies as well as the dominant frequencies showed significant variance (P < 0.01) amongst the different stages as well as cultivars (Table 2). A number of workers have reported drop in frequency magnitudes during ripening depending on the stage of senescence[21–22] during apple grading and storage.

Table 1 Data on resonance frequencies, damping ratio and firmness index of mango varieties at different stages of ripening (n = 6).

	Unripe		Semi ripe		Ripe		Ripe bruised
Cultivar	Resonance frequencies (Hz)	Damping ratio and stiffness index	Resonance frequencies (Hz)	Damping ratio	Resonance frequencies (Hz)	Damping ratio	Resonance frequencies (Hz)	Damping ratio
Neelam	366* ± 7.34	0.126 ± 0.003	318* ± 5.20	0.145 ± 0.002	246* ± 5.16	0.195 ± 0.004	58 ± 1.80	0.214 ± 0.005
	724 ± 12.45	(16.48 × 10^3)#	700 ± 15.78	(12.44 × 10^3 ± 0.26 × 10^3)#	376 ± 8.20	(7.44 × 10^3 ± 0.17 × 10^3)#	148* ± 3.26	(26.94 × 10^2 ± 0.57 × 10^2)#
		± 0.29 × 10^3			538 ± 8.91
Alphonso	610* ± 0.12	0.023 ± 0.001	326 ± 7.56	0.114 ± 0.002	218* ± 5.47	0.132 ± 0.003	170* ± 3.67	0.138 ± 0.001
		(57.68 × 10^3 ± 1.19 × 10^3)#	492* ± 11.85	(37.51 × 10^3 ± 1.08 × 10^3)#	434 ± 4.88	(73.66 × 10^2 ± 2.21 × 10^3)#	298 ± 4.82	(44.8 × 10^2 ± 1.28 × 10^2)#
					456 ± 6.29		424 ± 3.74
Bangana	548* ± 13.91	0.044 ± 0.002	192 ± 4.20	0.207 ± 0.004	174 ± 3.19	0.287 ± 0.006	212* ± 5.19	0.226 ± 0.004
palli	774 ± 23.83	(82.28 × 10^3± 1.92 × 10^3)#	368* ± 3.86	(37.11 × 10^3 ± 1.39 × 10^3)#	274* ± 7.78	(20.57 × 10^3 ± 0.55 × 10^3)#	428 ± 9.11	(12.31 × 10^3 ± 0.27 × 10^3)#
		#	712 ± 9.50		460 ± 13.75
Totapuri	310 ± 7.22	0.105 ± 0.002	300 ± 6.78	0.155 ± 0.001	276* ± 6.92	0.224 ± 0.002	172 ± 3.14
	488 ± 10.44	(83.72 × 10^3 ± 1.88 × 10^3)#	362* ± 8.21	(29.48 × 10^3 ± 1.02 × 10^3)#	336 ± 5.24	(17.14 × 10^3 ± 1.01 × 10^3)#	268* ± 2.86	0.268 ± 0.002
	610* ± 12.30		468 ± 10.67				344 ± 3.92	(16.16 × 10^3 ± 0.81 × 10^3)#
			615 ± 14.08
Mallige	502* ± 5.62	0.118 ± 0.001	400* ± 6.18	0.207 ± 0.002	186 ± 2.63	0.287 ± 0.001	106 ± 2.08
	872 ± 8.10	(73.08 × 10^3 ± 1.36 × 10^3)#	490 ± 5.20	(46.4 × 10^3 ± 1.04 × 10^3)#	314* ± 3.19	(28.59 × 10^3 ± 0.36 × 10^3)#	216* ± 3.74	0.319 ± 0.003
	166 ± 3.20		618 ± 10.18				398 ± 5.64	(13.53 × 10^3 ± 0.29 × 10^3)#
*Dominant frequencies for which damping ratio and stiffness indices are derived;
#Firmness indices for respective dominant frequencies.

Figure 2 Typical acoustic fourier transform power spectra in frequency domain of mango (cv. Neelam) at different stages of ripening and under bruising.

Table 2 Analysis of variance pertaining to dominant resonance frequencies and damping ratio of mango cultivars at different stages of ripening (n = 6).

		Resonance frequency		Damping ratio		Firmness index
Confidence level		CD	SEM	CD	SEM	CD	SEM
P < 0.01	a	5.496	1.461	0.6536	0.1737	17.358 × 10^3	4.5 × 10^3
	b	6.145	1.033	0.7308	0.1942	15.526 × 10^3	4.1 × 10^3
	a × b	12.29	3.267	0.1462	0.3885	34.717 × 10^3	9.0 × 10^3
		CD	SEM	CD	SEM	CD	SEM
P < 0.05	a	4.133	–	0.491	–	12.972 × 10^3	–
	b	4.62	–	0.549	–	11.602 × 10^3	–
	a × b	9.24	–	0.109	–	25.944 × 10^3	–
a: variety; b: ripening stage; CD = Critical Difference; SEM = Standard Error of Mean.

During the acoustic analysis, it was observed that the geometric configuration of the fruit bed was important apart from the type of sensor, fruit, and sensor contact, excitation method to obtain consistent frequency profiles. Shmulevich et al.[23] reported varied acoustic response by different geometric configurations of the sensing elements, fruit mass, and impulse locations of apple varieties. In the present study, different configurations of fruit bed were tested, i.e., flat, conical, and suspended types, and the flat bed was found to ensure a better fruit and sensor contact. The PVDF based film sensors were found to give consistent frequency profiles without background frequencies. The excitation methods included dynamic excitation, as well as use of impedence tube. Use of impedence tube was found to result in suppression of major frequencies causing erratic frequency profiles. Different types of excitation materials at the tip of the metal used in the hammer based dynamic excitation process also gave varied consistency in terms of acoustic profiles. Rubber and acrylic tipped hammers were used as excitation sources between which acrylic tipped hammer gave consistent profiles independent of the ripening stage. The geometric configuration of the sensor at 180° to the vertical axis of the fruit at the specified ranges of fruit mass and sensor area were found to be optimal in giving consistent acoustic profiles of different cultivars with variations in shape and size. These experimental parameters need to be taken into consideration for the development of online acoustic process with micro chip controlled cycles of excitation and movement of fruits on a conveyor system.

Damping Ratio and Fruit Firmness Index

The data on damping ratio and firmness index (Table 1) showed progressive increase in the damping ratio along with a concomitant decrease in the firmness index. Damping ratio as a measure of softening of the fruit had been reported as an effective index to evaluate the degree of fruit softening for avocado.[17] As such the damping ratio indicates the drag between the onset and completion of a frequency, and the same showed a consistent increment with progressive ripening stages. Abbot and Liljedahl[24] suggested the incorporation of second frequencies in the computation of firmness index based on acoustic data, as well as the fruit mass for apples. However, in the case of mango cultivars consideration of dominant frequencies for the calculation of firmness index was found to give a better understanding and in concurrence with a simultaneous rise in damping ratio. Acoustic data based firmness indices are preferred due to the consideration of fruit mass which is an influencing entity. The damping ratio as well as the fruit firmness index were found to be significantly different (P < 0.01) amongst the different cultivars and also amongst the different ripening stages. Acoustic data based parameters such as damping ratio and firmness index can be highly useful in evaluating the degree of tissue softening during online sorting of mangoes.

Mechanical Test and Color

Texture and color are important sensory attributes of the fresh produce and high quality mangoes need to be sound in both the aspects to enhance the consumer appeal. In many cases the penetration test is correlated with the sensory texture. During the ripening of five cultivars, the fruits showed characteristic penetration force and tristimulus color values (Table 3). The L values indicating the transformation of fruit color in to a lighter shade, showed a progressive increment and the a/b values also showed a similar trend indicating the gradual development of yellowness from the initial green shades. The cultivars as well as the different ripening stages showed significant (P < 0.01) variance (Table 4). The interaction between the cultivars, and the ripening stages also showed significant variation (p < 0.01) suggesting diversity in the color at different ripening stages of the various cultivars.

Table 3 Changes in tristimulus colour profiles and penetrometric texture of mango cultivars at different stages of ripening (n = 6).

	L			a/b			Texture (N)
Cultivar	Unripe	Semi ripe	Ripe	Unripe	Semi ripe	Ripe	Unripe	Semi ripe	Ripe
Neelam	51.6 ± 1.08	58.63 ± 1.29	64.21 ± 1.46	0.047 ± 0.001	0.122 ± 0.003	0.208 ± 0.003	74.3 ± 1.60	47.5 ± 1.50	33.7 ± 0.30
Alphonso	42.2 ± 0.92	54.21 ± 1.21	59.26 ± 1.74	0.31 ± 0.006	0.036 ± 0.001	−0.221 ± 0.002	48.5 ± 1.10	36.0 ± 0.60	27.8 ± 0.30
Bangana palli	58.49 ± 1.31	64.86 ± 1.46	67.39 ± 2.10	−0.139 ± 0.003	0.263 ± 0.002	0.239 ± 0.002	57.6 ± 2.00	41.5 ± 0.80	26.3 ± 0.20
Totapuri	45.53 ± 1.36	57.28 ± 1.28	62.43 ± 1.37	−0.112 ± 0.002	0.176 ± 0.001	0.227 ± 0.003	62.3 ± 2.80	44.0 ± 0.50	28.5 ± 0.50
Mallige	46.71 ± 1.26	52.37 ± 1.35	58.63 ± 1.02	−0.107 ± 0.002	0.132 ± 0.002	0.136 ± 0.001	53.5 ± 2.40	38.0 ± 0.20	22.9 ± 0.10

Table 4 Analysis of variance pertaining to tristimulus color profile and penetrometric texture of mango varieties at different stages of ripening (n = 6).

		L		a/b		Texture
Confidence level		CD	SEM +	CD	SEM +	CD	SEM +
P < 0.1	a	1.617	0.43	0.0099 × 10^−2]	0.0026	0.226	0.594
	b	1.807	0.48	0.0128	0.0034	0.292	0.292
	a × b	3.615	0.96	0.0222	0.0058	0.505	0.505
P < 0.5	a	1.216	–	0.0074	–	0.169	–
	b	1.359	–	0.0096	–	0.218	–
	a × b	2.718	–	0.0166	–	0.378	–
a: variety; b: ripening stage; CD = Critical Difference; SEM = Standard Error of Mean.

The penetration test data showed varying extents of fruit softening. However, the heterogencity in ripening at different portions of mango tissue and the interference of a single large seed cause problems during the penetrometry. Penetrometry, being simple and suitable for routine means of firmness measurements in packing houses could be performed with higher accuracy by halving the fruit followed by puncture tests covering the cut end, middle, and distal ends of the fruits. The penetration test profile as such showed a significant decline with advancing stages of ripening. The different cultivars also showed varying extents of softening with the cultivar Neelam showing the maximum firmness and the cultivar Mallige showing minimum firmness upon ripening. The cultivars ripening stages as well as the interaction of cultivars with ripening stages showed significant variation (P < 0.01 and P < 0.05).

Correlations

The acoustic parameters i.e. dominant frequencies, first frequencies, damping ratio, and firmness index were correlated with mechanical penetration test and a/b values derived from the tristimulus color profile, and the varied correlations were obtained for different cultivars (Table 5). Several reports exist with regards to correlation of acoustic data in terms of resonance frequencies with conventional measurements of firmness.[25] Fruits such as apple received maximum attention as far as the correlations of acoustic data with firmness are concerned since apple is extensively subjected to online sorting to ensure appropriate quality for the packaged fruits. Several investigators have attempted to relate sonic measurements with puncture tests and obtained significant correlations between resonanance frequency and penetrometric firmness in the case of fresh produce, i.e., apples[26] and Watermelon.[27] The present study showed marked correlations between acoustic data, i.e., dominant frequency, first frequency, damping ratio, and firmness based on dominant frequency, with the penetration test. The maximum correlations were obtained for the interaction between penetration test and firmness index.

Table 5 Data on correlation coefficients between values of resonance frequency, damping ratio, firmness index, and that of tristimulus color (a/b) and penetrometric texture for various mango cultivars.

		Resonance frequencies
Cultivar		Dominant frequency	First frequency	Damping ratio	Firmness index
Neelam	a) a/b	−0.597	−0.564	–	–
	b) Texture	0.821	0.812	−0.844	0.862
Alphonso	a) a/b	−0.621	−0.593	–	–
	b) Texture	0.915	0.889	−0.926	0.933
Banganapalli	a) a/b	−0.667	−0.612	–	–
	b) Texture	0.889	0.864	−0.896	0.916
Totapuri	a) a/b	−0.564	−0.416	–	–
	b) Texture	0.844	0.808	−0.859	0.864
Mallige	a) a/b	−0.653	−0.562	–	–
	b) Texture	0.832	0.814	−0.863	0.883

Though mango is a major tropical fruit, scanty information is available with regards to such correlations between acoustic data and texture and a/b color values to varying extents for different mango cultivars (Table 5). The dominant frequencies for all the five varieties showed consistently higher correlations with the penetration test along with the firmness indices derived, based on the mass of the fruit. The dominant frequency showed higher correlations compared to the first frequencies in the form of positive correlations. The damping ratio showed negative correlations with the progression in softening, indicating a significant relationship between damping ratio and penetration force. The extents of correlations were higher than dominant and first frequencies though lower than those of firmness indices.

The correlations suggest lesser extents of scatter in the acoustic measurements despite the tissue heterogencity associated with mangoes. The same can be attributed to the geometric stability of the mango cultivars in facilitating better fruit body contact with the piezo electric sensor film. The mode of acoustic excitation by means of imparting a direct excitation on the fruit surface with a plastic tipped hammer could also optimize generation of acoustic power spectra consistently in a flat bed system. The surface restraint by the skin was found to be negligible due to the direct excitation method. The correlation coefficients were consistently above 0.9 denoting the usefulness of different acoustic parameters to determine texture profile for the purpose of online sorting. However, there is a need to develop suitable automated process of acoustic excitation to derive fourier transforms depicting the specific acoustic parameters. In case of the firmness index, the inclusion of dominant frequency in the formula in lieu of second frequencies used for fruits such as apples was found to be more useful in the case of mangoes and the highest correlations were obtained for the same with penetration test. The interrelationships between mechanical testing of texture with either dominant wavelength or the damping ratio showed consistent trends depending on the stage of ripening of respective cultivars (Figs. 3, 4).

Figure 3 Interrelation between firmness and damping ratio of different cultivars of mango fruits.

Figure 4 Interrelation between firmness and dominant frequency of different cultivars of mango fruits.

In case of color, in terms of a/b values signifying the transition between greenish to reddish leading to a yellow tinge, the correlations with acoustic data showed the highest coefficient values with the dominant frequencies as compared to the first frequencies and damping ratio (Table 5). Reports are scanty regarding the correlations between color and acoustic data during the ripening of fruits. Certain reports exist with regards to significant correlations between texture and color parameters as in the case of Banana.[28] These reports suggest a possible indirect relationship between acoustic parameters and the color profile during the ripening of fruits. In case of mango, the correlations varied from 0.416 to 0.667 following the negative pattern. Cv. Banganapalli showed the maximum correlation where as Cv. Totapuri showed the minimum.

Bruising and Infestation

Bruising and internal infestation are formidable problems during the online sorting of mangoes apart from the formation of spongy tissue. Though major bruises could be picked up visually, often it is observed that minor bruises and hair line cracks escaped visual sorting and later on caused physiological disorders and infections. The acoustic data for mango cultivars showed a significant fall in dominant frequencies (P < 0.01) as compared to unbruised fruits of the same ripening stage (full ripe) (Table 1). The fall in dominant frequency was associated with an increment in the damping ratio of the different mango cultivars. Internal infestation is a common problem associated with mango cultivars such as Cv. Neelam. The acoustic data showed a significant fall in the magnitude of first frequency, while the dominant frequency showed a marginal increment (Fig. 2). The same trend was observed consistently for infested vis-a-vis healthy fruits of the same ripening stage (semi- ripe stage).

As such, the acoustic spectroscopic data showed consistent profiles for the five different cultivars at the three ripening stages. The correlations between acoustic data and penetration test were much higher compared to those with tristimulus color in terms of a/b values. The nondestructive methodology based on acoustics could be an effective quality control measure for the online sorting of mango fruits.

CONCLUSIONS

The acoustic spectroscopy could be an effective nondestructive method for mango cultivars, i.e., Neelam, Alphonso, Banganapalli, Totapuri, and Mallige for online sorting of the fruits for various blemishes such as excess softening and minor bruises. The application of piezo electric materials such as polyvinylidine fluoride coated in the form of a film could be used for obtaining consistent acoustic data without scatter for mango cultivars despite pronounced tissue heterogencity. The correlations between the dominant frequency, first frequency, damping ratio, and firmness index with penetration test at various ripening stages were consistent. Internal infestation of mango (Cv. Neelam) showed a definite trend by showing a significant fall in the first frequency value. Tristimulus color profile data in terms of a/b values showed lesser extents of correlation with the acoustic data compared to the penetration test. As such, the acoustic technique can be useful for online grading of mango cultivars as an online monitoring process in packing house operations.

ACKNOWLEDGMENT

The authors are thankful to Director, Naval Physical and Oceanographic Laboratory (NPOL, Kochi, India) for rendering technical help in the acoustic experimentation.